Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes
Reporter: Amandeep Kaur, B.Sc., M.Sc.
A recent study reports the development of an advanced AI algorithm which predicts up to five years in advance the starting of type 2 diabetes by utilizing regularly collected medical data. Researchers described their AI model as notable and distinctive based on the specific design which perform assessments at the population level.
The first author Mathieu Ravaut, M.Sc. of the University of Toronto and other team members stated that “The main purpose of our model was to inform population health planning and management for the prevention of diabetes that incorporates health equity. It was not our goal for this model to be applied in the context of individual patient care.”
Research group collected data from 2006 to 2016 of approximately 2.1 million patients treated at the same healthcare system in Ontario, Canada. Even though the patients were belonged to the same area, the authors highlighted that Ontario encompasses a diverse and large population.
The newly developed algorithm was instructed with data of approximately 1.6 million patients, validated with data of about 243,000 patients and evaluated with more than 236,000 patient’s data. The data used to improve the algorithm included the medical history of each patient from previous two years- prescriptions, medications, lab tests and demographic information.
When predicting the onset of type 2 diabetes within five years, the algorithm model reached a test area under the ROC curve of 80.26.
The authors reported that “Our model showed consistent calibration across sex, immigration status, racial/ethnic and material deprivation, and a low to moderate number of events in the health care history of the patient. The cohort was representative of the whole population of Ontario, which is itself among the most diverse in the world. The model was well calibrated, and its discrimination, although with a slightly different end goal, was competitive with results reported in the literature for other machine learning–based studies that used more granular clinical data from electronic medical records without any modifications to the original test set distribution.”
This model could potentially improve the healthcare system of countries equipped with thorough administrative databases and aim towards specific cohorts that may encounter the faulty outcomes.
Research group stated that “Because our machine learning model included social determinants of health that are known to contribute to diabetes risk, our population-wide approach to risk assessment may represent a tool for addressing health disparities.”
Ravaut M, Harish V, Sadeghi H, et al. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021;4(5):e2111315. doi:10.1001/jamanetworkopen.2021.11315 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780137
Other related articles were published in this Open Access Online Scientific Journal, including the following:
AI in Drug Discovery: Data Science and Core Biology @Merck &Co, Inc., @GNS Healthcare, @QuartzBio, @Benevolent AI and Nuritas
Reporters: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD
Improving diagnostic yield in pediatric cancer precision medicine
Elaine R Mardis
Advent of genomics have revolutionized how we diagnose and treat lung cancer
We are currently needing to understand the driver mutations and variants where we can personalize therapy
PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
The incidence rates and mortality rates of pediatric cancers are rising
Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics. Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors. RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations
They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
Need a diverse workforce across the cancer research and care spectrum
Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020
Tuesday, June 23
12:45 PM – 1:46 PM EDT
Virtual Educational Session
Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics
This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.
Judith A Varner, Yuliya Pylayeva-Gupta
Introduction
Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
Different type of cells are becoming targets for immune checkpoint like myeloid cells
In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
CXCLs are potential targets
PI3K delta inhibitors,
Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity
There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.
Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage
Discovering and optimizing covalent small-molecule ligands by chemical proteomics
Benjamin F Cravatt
Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
They use scout molecules to target ligands or proteins with reactive cysteines
For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)
Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier
The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib
Richard A. Ward
Cysteine activation: thiolate form of cysteine is a strong nucleophile
Thiolate form preferred in polar environment
Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
pKas of cysteine vary in EGFR
cysteine that are too reactive give toxicity while not reactive enough are ineffective
Accelerating drug discovery with lysine-targeted covalent probes
This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.
Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe
T-cell metabolism and metabolic reprogramming antitumor immunity
Jeffrey C Rathmell
Introduction
Jeffrey C Rathmell
Metabolic functions of cancer-associated fibroblasts
Mara H Sherman
Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response
Greg M Delgoffe
Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
When knock down glucose transporter the cells become more glycolytic
If patient had high oxidative metabolism had low PDL1 sensitivity
Showed this result in head and neck cancer as well
Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
Metformin now in clinical trials
T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
T cells from tumors have defective mitochondria or little respiratory capacity
They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy
Obesity, lipids and suppression of anti-tumor immunity
Lydia Lynch
Hypothesis: obesity causes issues with anti tumor immunity
Less NK cells in obese people; also produce less IFN gamma
RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
Upregulated genes that were upregulated involved in lipid metabolism
All were PPAR target genes
NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.
Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath
Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
However it will still take a trained pathologist to define regions of interest or field of desired view
Introduction
Jayanta Debnath
Challenges and barriers of implementing AI tools for cancer diagnostics
Jorge S Reis-Filho
Implementing robust digital pathology workflows into clinical practice and cancer research
Jayanta Debnath
Invited Speaker
Thomas J Fuchs
Founder of spinout of Memorial Sloan Kettering
Separates AI from computational algothimic
Dealing with not just machines but integrating human intelligence
Making decision for the patients must involve human decision making as well
How do we get experts to do these decisions faster
AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
In sandbox scenario the rules are set in stone and machines are great like chess playing
In second scenario can train computer to predict what a human would predict
So third scenario is like driving cars
System on constrained nature or constrained dataset will take a long time for commuter to get to decision
Fourth category is long term data collection project
He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
With an AI system pathologist drastically improved the ability to detect very small lesions
Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.
Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao
Cancers that are and are not increasing in younger populations
Stacey A. Fedewa
Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties. It is very hard for women as they are on their childbearing years so additional stress
Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer. Colorectal and testicular most common in US.
SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers
Other Articles on this Open Access Online Journal on Cancer Conferences and Conference Coverage in Real Time Include
Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education
Curator: Stephen J. Williams, PhD.
Dr. Cathy N. Davidson from Duke University gives a talk entitled: Now You See It. Why the Future of Learning Demands a Paradigm Shift
In this talk, shown below, Dr. Davidson shows how our current education system has been designed for educating students for the industrial age type careers and skills needed for success in the Industrial Age and how this educational paradigm is failing to prepare students for the challenges they will face in their future careers.
Or as Dr. Davidson summarizes
Designing education not for your past but for their future
As the video is almost an hour I will summarize some of the main points below
PLEASE WATCH VIDEO
Summary of talk
Dr. Davidson starts the talk with a thesis: that Institutions tend to preserve the problems they were created to solve.
All the current work, teaching paradigms that we use today were created for the last information age (19th century)
Our job to to remake the institutions of education work for the future not the one we inherited
Four information ages or technologies that radically changed communication
advent of writing: B.C. in ancient Mesopotamia allowed us to record and transfer knowledge and ideas
movable type – first seen in 10th century China
steam powered press – allowed books to be mass produced and available to the middle class. First time middle class was able to have unlimited access to literature
internet- ability to publish and share ideas worldwide
Interestingly, in the early phases of each of these information ages, the same four complaints about the new technology/methodology of disseminating information was heard
ruins memory
creates a distraction
ruins interpersonal dialogue and authority
reduces complexity of thought
She gives an example of Socrates who hated writing and frequently stated that writing ruins memory, creates a distraction, and worst commits ideas to what one writes down which could not be changed or altered and so destroys ‘free thinking’.
She discusses how our educational institutions are designed for the industrial age.
The need for collaborative (group) learning AND teaching
Designing education not for your past but for the future
In other words preparing students for THEIR future not your past and the future careers that do not exist today.
In the West we were all taught to answer silently and alone. However in Japan, education is arranged in the han or group think utilizing the best talents of each member in the group. In Japan you are arranged in such groups at an early age. The concept is that each member of the group contributes their unique talent and skill for the betterment of the whole group. The goal is to demonstrate that the group worked well together.
In the 19th century in institutions had to solve a problem: how to get people out of the farm and into the factory and/or out of the shop and into the firm
Takes a lot of regulation and institutionalization to convince people that independent thought is not the best way in the corporation
keywords for an industrial age
timeliness
attention to task
standards, standardization
hierarchy
specialization, expertise
metrics (measures, management)
two cultures: separating curriculum into STEM versus artistic tracts or dividing the world of science and world of art
This effort led to a concept used in scientific labor management derived from this old paradigm in education, an educational system controlled and success measured using
grades (A,B,C,D)
multiple choice tests
keywords for our age
workflow
multitasking attention
interactive process (Prototype, Feedback)
data mining
collaboration by difference
Can using a methodology such as scientific curation affect higher education to achieve this goal of teaching students to collaborate in an interactive process using data mining to create a new workflow for any given problem? Can a methodology of scientific curation be able to affect such changes needed in academic departments to achieve the above goal?
This will be the subject of future curations tested using real-world in class examples.
However, it is important to first discern that scientific content curation takes material from Peer reviewed sources and other expert-vetted sources. This is unique from other types of content curation in which take from varied sources, some of which are not expert-reviewed, vetted, or possibly ‘fake news’ or highly edited materials such as altered video and audio. In this respect, the expert acts not only as curator but as referee. In addition, collaboration is necessary and even compulsory for the methodology of scientific content curation, portending the curator not as the sole expert but revealing the CONTENT from experts as the main focus for learning and edification.
Other article of note on this subject in this Open Access Online Scientific Journal include:
The above articles will give a good background on this NEW Conceived Methodology of Scientific Curation and its Applicability in various areas such as Medical Publishing, and as discussed below Medical Education.
To understand the new paradigm in medical communication and the impact curative networks have or will play in this arena please read the following:
This article discusses a history of medical communication and how science and medical communication initially moved from discussions from select individuals to the current open accessible and cooperative structure using Web 2.0 as a platform.
In Data Science, A Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals: Analytics Designer, Aviva Lev-Ari, PhD, RN
An overview of Data Science as a discipline is presented in
On this landscape about IT, The Internet, Analytics, Statistics, Big Data, Data Science and Artificial Intelligence, I am to tell stories on my own pioneering work in data science, Algorithm-based decision support systems design for different organizations in several sectors of the US economy:
Images on 12/7/2019
Startups:
TimeØ Group – The leader in Digital Marketplaces Design
Concept Five Technologies, Inc. – Commercialization of DoD funded technologies
MDSS, Inc. – SAAS in Analytical Services
LPBI Group – Pharmaceutical & Media
Top Tier Management Consulting: SRI International, Monitor Group;
OEM: Amdahl Corporation;
Top 6th System Integrator: Perot System Corporation;
FFRDC: MITRE Corporation.
Publishing industry: was Director of Research at McGraw-Hill/CTB.
Northeastern University, Researcher on Cardiovascular Pharmacotherapy at Bouve College of Health Sciences (Independent research guided by Professor of Pharmacology)
Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI MA’76
Was prepared for publication in American Friends of the Hebrew University (AFHU), May 2018 Newsletter, Hebrew University’s HUJI AlumniSpotlight Section.
Aviva Lev-Ari’s profile was up on 5/3/2018 on AFHU website under the Alumni Spotlight at https://www.afhu.org/
On 5/11/2018, Excerpts were Published in AFHU e-news.
scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
4.2.5 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.
One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.
Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.
The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.
This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.
Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.
Precision Medicine has helped transform cancer care from one-size-fits-all chemotherapy to a new era, where patients’ tumors can be analyzed and therapy selected based on their genetic makeup. Until now, however, precision medicine’s impact has been far less in other therapeutic areas, many of which are ripe for transformation. Efforts are underway to bring the successes of precision medicine to neurology, immunology, ophthalmology, and other areas. This move raises key questions of how the lessons learned in oncology can be used to advance precision medicine in other fields, what types of data and tools will be important to personalizing treatment in these areas, and what sorts of partnerships and payer initiatives will be needed to support these approaches and their ultimate commercialization and use. The panel will also provide an in depth look at precision medicine approaches aimed at better understanding and improving patient care in highly complex disease areas like neurology.
Speaker panel: The big issue now with precision medicine is there is so much data and hard to put experimental design and controls around randomly collected data.
The frontier is how to CURATE randomly collected data to make some sense of it
One speaker was at a cancer meeting and the oncologist had no idea what to make of genomic reports they were given. Then there is a lack of action or worse a misdiagnosis.
So for e.g. with Artificial Intelligence algorithms to analyze image data you can see things you can’t see with naked eye but if data quality not good the algorithms are useless – if data not curated properly data is wasted
Data needs to be organized and curated.
If relying of AI for big data analysis the big question still is: what are the rates of false negative and false positives? Have to make sure so no misdiagnosis.
Please follow LIVE on TWITTER using the following @ handles and # hashtags:
Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL
Author and Curator: Aviva Lev-Ari, PhD, RN
The source for the inspiration to write this curation is described in
Unlike that little cancer conference in Chicago last week, the BIO International convention is not about data, but about the people who make up the biopharma industry.
The meeting brings together scientists, board members, business development heads and salespeople, from the smallest virtual biotechs to the largest of pharmas. It allows executives at fledgling biotechs to sit at the same tables as major decision-makers in the industry — even if it does look a little bit like speed dating.
But it’s not just a partnering meeting.
This year’s BIO also sought to shine a light on pressing issues facing the industry. Among those tackled included elevating the discussion on gender diversity and how to bring more women to the board level; raising awareness around suicide and the need for more mental health treatments; giving a voice to patient advocacy groups; and highlighting the need for access to treatments in developing nations.
Four days of meetings and panel discussions are unlikely to move the needle for many of these challenges, but debate can be the first step toward progress.
I attended the meetings on June 4,5,6, 2018 and covered in Real Time the sessions I attended. On the link below, Tweets, Re-Tweets and Likes mirrors the feelings and the opinions of the attendees as expressed in real time using the Twitter.com platform. This BioTechnology events manifested the AUTHENTICITY of Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL.
The entire event is covered on twitter.com by the following hash tag and two handles:
Part 2: Top 10 books to help you survive the digital age
From Philip K Dick’s obtuse robots to Mark O’Connell’s guide to transhumanism, novelist Julian Gough picks essential reading for a helter skelter world
Here are 10 of the books that did help me [novelist Julian Gough]: they might also help you understand, and survive, our complicated, stressful, digital age.
Marshall McLuhan Unbound by Marshall McLuhan (2005) The visionary Canadian media analyst predicted the internet, and coined the phrase the Global Village, in the early 1960s. His dense, complex, intriguing books explore how changes in technology change us. This book presents his most important essays as 20 slim pamphlets in a handsome, profoundly physical, defiantly non-digital slipcase.
Ubik by Philip K Dick (1969) Pure pulp SF pleasure; a deep book disguised as a dumb one. Dick shows us, not a dystopia, but a believably shabby, amusingly human future. The everyman hero, Joe Chip, wakes up and argues with his robot toaster, which refuses to toast until he sticks a coin in the slot. Joe can’t do this, because he’s broke. He then has a stand-up row with his robot front door, which won’t open, because he owes it money too … Technology changes: being human, and broke, doesn’t. Warning: Dick wrote Ubik at speed, on speed. But embedded in the pulpy prose are diamonds of imagery that will stay with you for ever.
The Singularity Is Near by Ray Kurzweil (2005) This book is what Silicon Valley has instead of a bible. It’s a visionary work that predicts a technological transformation of the world in our lifetime. Kurzweil argues that computer intelligence will soon outperform human thought. We will then encode our minds, upload them, and become one with our technology, achieving the Singularity. At which point, the curve of technological progress starts to go straight up. Ultimately – omnipotent, no longer mortal, no longer flesh – we transform all the matter in the universe into consciousness; into us.
To Be a Machine by Mark O’Connell (2017) This response to Kurzweil won this year’s Wellcome prize. It’s a short, punchy tour of transhumanism: the attempt to meld our minds with machines, to transcend biology and escape death. He meets some of the main players, and many on the fringes, and listens to them, quizzically. It is a deliberately, defiantly human book, operating in that very modern zone between sarcasm and irony, where humans thrive and computers crash.
A Visit from the Goon Squad by Jennifer Egan (2011) This intricately structured, incredibly clever novel moves from the 60s right through to a future maybe 15 years from now. It steps so lightly into that future you hardly notice the transition. It has sex and drugs and rock’n’roll, solar farms, social media scams and a stunningly moving chapter written as a PowerPoint presentation. It’s a masterpiece. Life will be like this.
What Technology Wants by Kevin Kelly (2010) Kelly argues that we scruffy biological humans are no longer driving technological progress. Instead, the technium, “the greater, global, massively interconnected system of technology vibrating around us”, is now driving its own progress, faster and faster, and we are just caught up in its slipstream. As we accelerate down the technological waterslide, there is no stopping now … Kelly’s vision of the future is scary, but it’s fun, and there is still a place for us in it.
The Meme Machine by Susan Blackmore (1999) Blackmore expands powerfully and convincingly on Richard Dawkins’s original concept of the meme. She makes a forceful case that technology, religion, fashion, art and even our personalities are made of memes – ideas that replicate, mutate and thus evolve over time. We are their replicators (if you buy my novel, you’ve replicated its memes); but memes drive our behaviour just as we drive theirs. It’s a fascinating book that will flip your world upside down.
Neuromancer by William Gibson (1984) In the early 1980s, Gibson watched kids leaning into the screens as they played arcade games. They wanted to be inside the machines, he realised, and they preferred the games to reality. In this novel, Gibson invented the term cyberspace; sparked the cyberpunk movement (to his chagrin); and vividly imagined the jittery, multi-screened, anxious, technological reality that his book would help call into being.
You Are Not a Gadget: A Manifesto by Jaron Lanier (2010) Lanier, an intense, brilliant, dreadlocked artist, musician and computer scientist, helped to develop virtual reality. His influential essay Digital Maoism described early the downsides of online collective action. And he is deeply aware that design choices made by (mainly white, young, male) software engineers can shape human behaviour globally. He argues, urgently, that we need to question those choices, now, because once they are locked in, all of humanity must move along those tracks, and we may not like where they take us. Events since 2010 have proved him right. His manifesto is a passionate argument in favour of the individual voice, the individual gesture.
All About Love: New Visions by bell hooks (2000) Not, perhaps, an immediately obvious influence on a near-future techno-thriller in which military drones chase a woman and her son through Las Vegas. But hooks’s magnificent exploration and celebration of love, first published 18 years ago, will be far more useful to us, in our alienated digital future, than the 10,000 books of technobabble published this year.All About Love is an intensely practical roadmap, from where we are now to where we could be. When Naomi and Colt find themselves on the run through a militarised American wilderness of spirit, when GPS fails them, bell hooks is their secret guide.
Part 3: A case study on Thriving at the Survival Calls during Careers in the Digital Age: Aviva Lev-Ari, UCB, PhD’83; HUJI, MA’76
On June 10, 2018
Following, is a case study about an alumna of HUJI and UC, Berkeley as an inspirational role model. An alumna’s profile in context of dynamic careers in the digital age. It has great timeliness and relevance to graduate students, PhD level at UC Berkeley and beyond, to all other top tier universities in the US and Europe. As presented in the following curations:
Professional Self Re-Invention: From Academia to Industry – Opportunities for PhDs in the Business Sector of the Economy
Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI, MA’76
Unemployment figures of PhDs by field of science are included, Ankita Gurao identifies the following four alternative careers for PhDs in the non-academic world:
Phase 2: Corporate Applied Research in the US, 1985 – 2005
Phase 3: Career Reinvention in Health Care, 2005 – 2012
Phase 4: Electronic Scientific Publishing, 4/2012 to present
These four phases are easily mapped to the four alternative careers for PhDs in the non-academic world. One can draw parallel lines between the four career opportunities A,B,C,D, above, and each one of the four phases in my own career.
Namely, I have identified A,B,C,D as early as 1985, and pursued each of them in several institutional settings, as follows:
A. Science Writer/Journalist/Communicator – see link above for Phase 4: Electronic Scientific Publishing, 4/2012 to present
B. Science Management – see link above for Phase 2: Corporate Applied Research in the US, 1985 – 2005 and Phase 3: Career Reinvention in Health Care, 2005 – 2012
C. Science Administration – see link above for Phase 2: Corporate Applied Research in the US, 1985 – 2005and Phase 4: Electronic Scientific Publishing, 4/2012 to present
D. Science Entrepreneurship – see link above for Phase 4: Electronic Scientific Publishing, 4/2012 to present
Impressions ofMy Days at Berkeley in Recollections: Part 1 and 2, below.
Recollections: Part 1 – My days at Berkeley, 9/1978 – 12/1983 –About my doctoral advisor, Allan Pred, other professors and other peers
The topic of Careers in the Digital Age is closely related to my profile, see chiefly: Four-phase Career,Reflections, Recollections Parts 1 & 2 and information from other biographical sources, below.
Pioneering implementation of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI, MA’76
Author: Aviva Lev-Ari, PhD, RN
May 24, 2018
April 12. 2017
INTRODUCTION
In 1975, while a Masters student at the Hebrew University in Jerusalem (HUJI), I attended a graduate course, “Methodology Development and Theory Construction in the Social Sciences”. The course was taught by Prof. Louis Guttman. He arrived in Israel in 1948 from Cornell University to establish the measurement concentration in cognitive sciences in the psychology department at HUJI. He established the Applied Research Institute in Social Sciences, where public opinion studies were carried out for fifty years. Dr. Shlomit Levy, a key collaborator of Prof. Guttman, was the teaching assistant for the class. Every Masters student across all the departments of the social sciences faculty, planning to write a Master thesis enrolled in this course, one semester for five hours a week.
It had two major project submissions and two exams. It was considered the most difficult course at HUJI. I got [A minus] and was stimulated and attracted to the course domain for the 25 years that followed.
Following this course, I attended an advanced course by Professor Chaim Adler:
in the Department of Sociology on multivariate analysis, and have used ADDTREE, a software developed by Prof. Amos Tversky and his programmer, a PhD student in the mathematics department at HUJI, Shmuel Sattath, who assisted me with SPSS on my Master thesis data base, which had 200 subjects and 42 variables and was considered a large data set for SPSS in 1975. Mr. Sattath recommended ADDTREE. The programming functions were taken over by Amnon Antebi, who worked with me on MSA, POSA, and ADDTREE, carrying two heavy boxes of computer punched cards for the CDC mainframe computer at the Center for Computation at HUJI. Antebi, as a professional mainframe computer programmer, alone could submit jobs and pick up the printed output, which was placed in bins alphabetically by the last name of the programmer.
Professor Louis Guttman was the developer of the Guttman scale, MDS: MSA, SSA, and POSA, and many other algorithms used originally in psychometrics since 1880. The field is concerned with the objective measurement of skills and knowledge, abilities, attitudes, personality traits, and educational achievement. Assessment tools such as questionnaires, tests, raters’ judgments, and personality tests were constructed and adopted, and these became the foundation of quantitative modeling in the social sciences since the 1930s.
In this course I learned MDS: MSA, SSA, POSA and to design questionnaires. I designed one for my Masters thesis and applied it to two samples with 100 heads of household in each sample. I applied the Kolmogorov-Smirnov test for a two-sample comparison and applied the ADDTREE clustering algorithm to compare the results of dimensionality reduction of 42 variables by MDS vs ADDTREE, This was the first application of
ADDTREE software to consumer preferences
MDS to consumer choice under constraints
The thesis grade contributed to the final Master GPA. I was told by the graduate office that my GPA was the highest grade ever awarded for a Masters degree in social sciences at HUJI until 1976.
Of all the courses I took at HUJI during the six years of my enrollment for a BA and an MA – it was Prof. Guttman and Prof. Adler’s courses that set off my career in quantitative methods from the start of the Masters thesis for the next 25 years, performing creative data modeling and analysis as a profession.
While working at SRI, I contacted Yissum, the HUJI’s technology transfer office (TTO) for licensing the MDS software, written by Reuven Amar, at SRI International. We applied MSA and SSA on GM data and in several other studies. This was the second time that I licensed the software from HUJI.
I cherish the correspondence I had with Prof. Louis Guttman following my hiring at SRI International. He was very proud to know that his student was using MSA for General Motors management decision making on selective divestiture of their auto parts division. He knew SRI International, as an R&D institution, very well for its projects in education, biostatistics and genetics (his wife, Prof. Ruth Guttman, was professor of Genetics at Cornell and HUJI.)
I visited him in 1986 in Jerusalem, showing him the computer output of the data from the GM project. Of course, he had important insights into the interpretation of the results. I sent him a copy of a professional movie made on the GM model that I designed. The VCR cassette was returned to me by his daughter in New Jersey following his death, 10/25/1987. He received it at the hospital. He knew about it but was unable to watch the movie, I was told.
The first time I licensed the MDS software from Yissum, was for teaching purposes at UC Berkeley, 1979, 1980, and 1981.
Upon my admission to the PhD Program at UC Berkeley, Prof. Pred arranged for me a teaching assistantship for an upper division course, three semesters in Quantitative Methods. This was the last course before graduation for any concentration in Letters & Sciences. The course was attended by students from geography, political sciences, political economics, economics, archeology, city planning, and botany. Any student that wished to learn about multivariate classification and prediction modeling enrolled.
It was a great privilege to write recommendation letters in February for a student graduating in May 1982. Some told me that “this is the only course that will get me a job.” It turned out that, that was true for myself as well, referring to Prof. Guttman’s course. Following the graduation from the Masters program at HUJI, I was hired at the Technion, IIT, because I mastered non-linear modeling and in particular MDS: MSA, SSA, and POSA.
During my career, I had the opportunity to design numerous one-of-a-kind models which represent pioneering implementations of analytics. A complete list is documented in the sources, below (List of Publications, 1983-2004). The very salient ones that represent milestones in the profession and the first application of these algorithms in these specific domain knowledge, include the following selective list:
Application of Multidimensional Scaling (MDS) for decomposition of consumer multivariate preference function, Master thesis, HUJI, 1976
Application of Multidimensional Scaling (MDS) for classification of urban municipalities in Israel for resource allocation of Ministry of Transportation road safety budget, Technion, TRC, RSC, 1977-1978
Multivariate analysis of product portfolios across 27 leading American paper companies for industrial concentration assessment and corporate benchmarking in sector context. PhD dissertation, UC Berkeley, 1983.
Application of Multidimensional Scaling (MDS) for SRI International’s clients: Competitive Assessment: Automotive. That contribution is mentioned in the 1987 Annual Report. Technology Assessment: Chemical and Allied Products, Resource Allocation Modeling in Advanced Material, Credit Scoring problem for clients in the Financial Sectors: Banking & Insurance
Demand Forecasting Model for Hardware, Amdahl Corporation. This model led to 1989 Employee Award.
Design of a Digital Market Place for Analytical Services at Concept Five Technologies, Inc.1996.
Design of Analytics suite of services for Digital Marketplaces: lumber, hospital supplies, MRO and consumables, PSC, 2007-2001. This modeling effort led to a distinguish bonus award,1999.
Adaptive Testing at McGraw-Hill, 2002, application of inverted simulation annealing algorithm for prediction of maximum functions in achievement scores.
Unemployment figures of PhDs by field of science are included, Ankita Gurao identifies the following four alternative careers for PhDs in the non-academic world:
Phase 2: Corporate Applied Research in the US, 1985 – 2005
Phase 3: Career Reinvention in Health Care, 2005 – 2012
Phase 4: Electronic Scientific Publishing, 4/2012 to Present
These four phases are easily mapped to the four alternative careers for PhDs in the non-academic world. One can draw parallels between the four career opportunities A,B,C,D, above, and each one of the four phases in my own career.
Namely, I have identified A,B,C,D as early as 1985, and pursued each of them in several institutional settings, as follows:
A. Science Writer/Journalist/Communicator – see link above for Phase 4: Electronic Scientific Publishing, 4/2012 to Present
B. Science Management – see link above for Phase 2: Corporate Applied Research in the US, 1985 – 2005 and Phase 3: Career Reinvention in Health Care, 2005 – 2012
C. Science Administration – see link above for Phase 2: Corporate Applied Research in the US, 1985 – 2005 and Phase 4: Electronic Scientific Publishing, 4/2012 to Present
D. Science Entrepreneurship – see link above for Phase 4: Electronic Scientific Publishing, 4/2012 to Present
Corporate Applied Research in the US, 1985 – 2005 – Data Science at its BEST
Twenty years of top-tier management consulting and as a corporate executive. In the corporate world, I worked for Fortune 50, using the principles of Statistical Modeling, Economic Geography and of Industrial Organization Economics, every day.
1985-2005, worked at Director Level with Start-ups and Fortune 100 companies making presentations at the CEO Board Room level. Is reinventing work so that it works for the digital global economy. Consults and speaks to large and small groups. Lead webinars for universities, corporations and associations, and generally stir up conversation and inquiry on industry trends. Startups: TimeØ Group, Concept Five Technologies, Inc., MDSS, Inc.; Top Tier Management Consulting: SRI International, Monitor Group; OEM: Amdahl Corporation; Top 6th System Integrator: Perot System Corporation; FFRDC: MITRE Corporation. In the Publishing industry: was Director of Research at McGraw-Hill/CTB. Researcher on Cardiovascular Pharmaco-therapy at Bouve College of Health Sciences, Northeastern University.
In 4/2012, have launched LPBI Group. She is an Executive-Entrepreneur with high energy and passion. Followed by 6200 Biotech professionals on LinkedIn and Group Manager of three Groups on LinkedIn.
Cardiovascular pharmacology-therapy research, development of a combination drug therapy, 2006-2007, Northeastern University, with Prof. Paul Aburjaily, PharmD
In 2015, Margot Gerritsen, director of Stanford’s Institute for Computational and Mathematical Engineering, got tired of technical conferences that included no or few women speakers. “I always joke that this meeting was a revenge effect,” she said. “We wanted to showcase really amazing work that’s being done by women.”
Now, in its third year, the Women in Data Science conference included 17 women speakers and roughly 100,000 people listening on live stream or Facebook Live. More than 170 regional events in over 50 countries also featured their own panels of women speakers. Gerritsen, who is also a professor of energy resources engineering, said one reason for the meeting is to inspire women to enter and stay in the field of data science. “It’s still really tough for women not to feel a little isolated,” she said.
One outcome of the event has been lists of women worldwide who can speak about data science that are now regularly provided to meeting organizers looking for women speakers. “I would never have imagined that we would be reaching so many people,” Gerritsen said.
Women who attended the meeting reflected on their own experiences and the value of a community of inspiring women.
“As we have more women involved in data science and computer science and machine learning, companies can be shaped more by women. I think it’s always better to have a diverse perspective. Taking a different approach might lead to different conclusions or different innovations, both in terms of theory and in terms of products that are changing the way we live. More balanced input from both men and women would be beneficial for everyone.” —Lan Huong Nguyen, PhD candidate, computational and mathematical engineering
“I saw so many data science conferences where there were no female speakers or just one or two. I would ask the organizers why are there no women. One time they said, ‘Well, Margot, you couldn’t make it, so that’s why.’ At some point we said it is so hard to get existing conferences to change and we wanted to just totally cancel any argument that you cannot find excellent women. That first WiDS conference we live streamed because we thought it would be nice to try. It was such a success and we realized, ‘Oh my goodness, we are hitting something that people are so hungry for.’ But it is a bit pathetic we still have to do this in 2018. Honestly, there are so many fabulous women. At the start today I joked, ‘When you look at the program you see technical panels with really outstanding work by outstanding people and you may realize they are all women. We really tried to find some male data scientists but we just couldn’t find any.’” —Margot Gerritsen, PhD ’97, director of the Institute for Computational and Mathematical Engineering
“Lately I’ve been trying to find inspirational women and looking for a role model, especially in STEM fields. It’s good to see that you have all these opportunities and there are people doing interesting things. People like you. It opens up a world of opportunities. In my country when I was growing up, women were expected to enter fields of either education or medicine, but I didn’t find myself in either field. I was on a scholarship from a company to study geophysics, but beyond the technical expertise the job required I didn’t know what other things I could do. I think this meeting shows you what options you have out there and how far you can go. It makes me believe in myself more and what I could do and the difference I can make in the world when I see all these women making a difference.” —Fatimah Al-Ismail, PhD candidate, geophysics
“Data science occurred to me a year or so ago as a way to bring together the aspects I love about history with the skills I enjoy about math. Because the subjects I’m studying don’t lead directly into an application of data science, it was very cool this morning to see women applying data science in a range of ways and realizing how many options there are to be excited about. Over the next year, I will be writing a thesis on the way American media interpreted and potentially influenced the Tiananmen Square protests in 1989. I have been trying to figure out how to incorporate a data study or data visualization into that project. Being here today makes me much more comfortable about reaching out to people at Stanford and excited to talk and ask for advice.” —Emily Shaw, ’19, history and math
“The very first time I was in a group of women like this was as a speaker at the conference for undergraduate women in physics. For me, that first time speaking to a group of women was totally mind-blowing. It’s just such an interesting energy. It feels so warm. I’m in a field that is about 15 percent women. In fields like mine and in certain areas of tech, some women do persist, but it’s not like it’s just the best women. It’s the women who are willing to put up with a certain level of isolation, and that means we are losing a lot of good people. I love my life and I love my job and I love my work, and it’s an incredible privilege to think about the biggest questions that we have. I just wish there were people from a broader set of groups who were able to ask those questions. Those are really universal questions that matter to everybody.” —Risa Wechsler, associate professor of physics and of particle physics and astrophysics
“When I was doing my PhD, the joke was that if you told people you were a statistician, then it would end the conversation. That’s no longer the case. Now people are interested in statistics and data science and machine learning. It’s really fun to be part of a field that people appreciate and see a need for. I don’t think a conference like this would have been possible 5 or 10 years ago. It’s really wonderful to see enthusiasm for data science, and especially enthusiasm among young women today for data science. It’s so important for these young budding data scientists to have the opportunity to interact with people who are more senior in the field and to have role models. We’ve heard talks about how it’s important to take risks in your career. I think there are a lot of risk takers in that room, and it’s pretty inspiring.” —Daniela Witten, BS ’05, MS ’06, PhD ’10, associate professor of statistics and biostatistics, University of Washington
“My mom was a computational mathematician. It seemed normal to me growing up. Then as I grew older, I saw that it actually wasn’t that common. That’s why this meeting is so important for young girls to see these strong figures. Just to see so many strong women in the room together and know how that can encourage girls through the process is very important and powerful. I’ll be joining Amazon as a data science researcher, so it is encouraging to see people not only from academia but also in industry. It makes me feel more confident moving forward.” —Danielle Maddix, PhD candidate, computational and mathematical engineering
“When I first got interested in science, I really wasn’t aware that there was this disparity. I now realize that it’s such a big issue. It’s really important that there are events like this where you are inspiring other women to be interested in science and saying it’s OK. I have been working for the Institute for Computational and Mathematical Engineering and helping organize a list of potential speakers for this conference, and it’s been so exciting to read about all the women and their accomplishments. I’m interested in combining computer science with my bioengineering major, and I know those fields involve a lot of data science. That’s why I’m here. Seeing the speakers in person and seeing the passion they have about their fields and how far they’ve come is amazing. You can really feel the energy in the room.” —Bianca Yu, ’20, bioengineering
“I run the big math women’s group. As a grad student here I was a member of the mechanical engineering women’s group, and that was hugely important in my student experience. I tried to replicate that now that I’m a position to help. A lot of our women graduate students in math broadly speaking sometimes find themselves a little bit isolated, so we’re trying to build more of a sense of a community for them. They may be the only women in the research group or one of only a handful in their department or program. If we want to keep people in the pipeline, we don’t want them to be discouraged. I got really jazzed today when I got to sit in a room full of women in a technical meeting. Being here was a day of inspiration that I allowed myself in my really busy schedule.” —Alison Marsden, associate professor of pediatrics and of bioengineering
“One fact about Stanford Graduate School of Education is that it is extremely interdisciplinary, and there’s quite a lot of active research that’s quantitative in nature. So I took statistics and data science classes and economics along the way. It turns out that I had this very transferable skill set. I started out thinking it would be fun to do a summer internship at a tech company. Then I realized the work was amazing and I wanted to go for a full-time job. One of the really great things for women in industry is that there is a ton of momentum, especially around women in data science. Certainly there are fewer women in leadership positions. I’m very glad to be able to buck that trend, and hopefully that’s encouraging to others as well. Data science also has an advantage because it’s a new field, and people from different industries can enter. Some of those have more diversity, so it’s a natural advantage in creating a more diverse network.” —Elena Grewal, MA ’11, PhD ’12, head of data science, Airbnb
“I am not technical myself. I’m a marketing person. But I’m a data-driven marketing person and I worked in tech for a lot of years, so I am always looking for ways to push the envelope for women. I like the fact that we’re able to bring this to the world beyond Stanford. With regional events, we’re able to highlight women from those regions, so women who might not have other speaking opportunities have a platform from which to share their research and to share their work. We actually were really trying hard to be in Saudi Arabia last year and we did not quite get there. And then this year we ended up having three regional events in Saudi Arabia. These are amazing women who are speaking. I think it’s wonderful that even in a place where women don’t really have a voice, they have a voice through WiDS.” —Judy Logan, co-director, Women in Data Science conference
“I recall many times I was the only woman engineer in a team and rarely ever saw a woman role model. I’d love to see more women in engineering pursuing advanced degrees and going to the next stages in their careers. We were brainstorming at the Institute for Computational and Mathematical Engineering several years ago on what to do, and we thought to have real impact we need to show those role models and inspire the next generation so that they can say, ‘Oh, I see myself in that person.’ That was really missing. We decided to pilot a Women in Data Science conference. We had 6,000 people pick it up on live stream the first year, so that’s when we thought, ‘Aha, we hit a chord here and we could have a much bigger impact if we try to reach out across the world.’ The question is then what do you do with all that bubbled-up energy and inspiration. That’s the most exciting thing – to see women take action and pursue their dreams.” —Karen Matthys, MBA ’88, co-director, Women in Data Science conference
Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and unity. DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs. As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy. And for utility, data science can fit empirical behavior to provide useful model where good theory doesn’t yet exist. That is, it can predict “what” is likely even when “why” is beyond reach.
But only if we do it right! The most vital data scientist skill is recognizing analytic hazards. With that, we become indispensable.
About John Elder:
John Elder, Ph.D., chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, and Washington D.C. Dr. Elder co-authored 3 award winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia, and was named by President Bush to serve 5 years on a panel to guide technology for national security.
Dr. Elder’s keynote is not the only proof of PAW’s continued commitment to hosting the brightest, sharpest minds in data science and machine learning. When you attend PAW in Las Vegas this June you will witness only the the most inspirational keynotes and actionable workshops including:
Predictive Analytics World is the place to meet peers, new partners, and get up to speed on the industry’s latest developments and opportunities. The networking is like no other. Don’t miss the biggest PAW event to date.
The first priority cited by the vice president was data sharing. Biden defended the concept as essential to advancing the process of cancer research and countered a January 21 New England Journal of Medicine editorial in which editor-in-chief Jeffrey Drazen, M.D., contended that data sharing could breed data “parasites.”
Four days later, Dr. Drazen clarified NEJM’s position by adding that with “appropriate systems” in place, “we will require a commitment from authors to make available the data that underlie the reported results of their work within 6 months after we publish them.”
Other priorities Biden said should serve as the basis of new incentives:
Involve patients in clinical trial design—Raising awareness of trials, and allowing patients to participate in how they are designed and conducted, could help address the difficulty of recruiting patients for studies. Only 4% of cancer patients are involved in a trial, he said.
“Let scientists do science”—Biden contrasted unfavorably NIH’s roughly 1-year process for decisions on grants to that of the Prostate Cancer Foundation, which limits grant applications to 10 pages and decides on those funding requests within 30 days: “Why is it that it takes multiple submissions and more than a year to get an answer from us?” Biden said.
Encourage grants from younger researchers—Biden decried the current professional system under which younger researchers are sidetracked for years doing administrative work in labs before they can pursue their own research grants: “It’s like asking Derek Jeter to take several years off to sell bonds to build Yankee Stadium,” the VP quipped.
Measure progress by outcomes—Rather than the quantity of research papers generated by grants, Biden said, “what you propose and how it affects patients, it seems to me, should be the basis of whether you continue to get the grant.”
Promote open-access publication of results—Biden criticized academic publishing’s reliance on paid-subscription journals that block content behind paywalls and which own data for up to a year. He contrasted that system with the Bill and Melinda Gates Foundation’s stipulation that the research it funds be published in an open-access journal and be freely available once published.
Reward verification—Research that verifies results through replication should be encouraged, Biden said, which acknowledging that few people now get such funding.
Biden recalled how following Beau’s diagnosis with cancer, he and his wife Jill Biden, Ed.D., who introduced the VP at the AACR event, “had access to the best doctors in the world.”
“The more we talked to them, the more we understood that we are on the cusp of a real inflection point in the fight against cancer.”
Updated 4/12/2019
Pediatric Cancer Initiatives
Data Sharing for Pediatric Cancers: President Trump Announces Pledge to Fight Childhood Cancer Will Involve Genomic Data Sharing Effort
In the journal Science, Drs. Olena Morozova Vaske ( and David Haussler University of California, Santa Cruz) recently wrote an editorial entitled “Data Sharing for Pediatric Cancers“, in which they discuss the implications of President Trump’s intentions to increase funding for pediatric cancers with a corresponding effort for genomic data sharing. Also discussed is the current efforts on pediatric genomic data sharing as well as some opinions on coordinating these efforts on a world-wide scale to benefit the patients, researchers, and clinicians.
The article is found below as it is a very good read on the state of data sharing in the pediatric cancer field and offers some very good insights in designing such a worldwide system to handle this data sharing, including allowing patients governance over their own data.
Last month, in a conference call held by the U.S. Department of Health and Human Services and National Institutes of Health (NIH), it was revealed that a large focus of President Trump’s pledge to fund childhood cancer research will be genomic data sharing. Although the United States has only 5% of the world’s pediatric cancer cases, it has disproportionately more resources and access to genomic information compared to low-income countries. We hope that the spotlight on genomic data sharing in the United States will galvanize the world’s pediatric cancer community to elevate genomic data sharing to a level where its full potential can finally be realized.
Pediatric cancers are rare, affecting 50 to 200 children per million a year worldwide. Thus, with 16 different major types and many subtypes, no cancer center encounters large cohorts of patients with the same diagnosis. To advance their understanding of particular cancer subtypes, pediatric oncologists must have access to data from similar cases at other centers. Because subtypes of pediatric cancer are rare, assembling large cohorts is a limiting factor in clinical trials as well. Here, too, data sharing is the first critical step.
Typically, pediatric cancers don’t have the number of mutations that make immunotherapies effective, and only a few subtypes have recurrent mutations that can be used to develop gene-targeted therapies. However, the abnormal expression level of genes gives a vivid picture of genetic misregulation, and just sharing this information would be a huge step forward. Using gene expression and mutation data, analysis of genetic misregulation in different pediatric cancer subtypes could point the way to new treatments.
A major challenge in genomic data sharing is the patient’s young age, which frequently precludes an opportunity for informed consent. Compounding this, the rarity of subtypes requires the aggregation of patients from multiple jurisdictions, raising barriers to assembling large representative data sets. A greater percentage of children than adults with cancer participate in research studies, and children often participate in multiple studies. However, this means that data collected on individual children may be found at multiple institutions, creating difficulties if there are no standards for data sharing.
To enable effective sharing of genomic and clinical data, the Global Alliance for Genomics and Health has developed the Key Implications for Data Sharing (KIDS) framework for pediatric genomics. The recommendations include involving children in the data-sharing decision-making process and imposing an ethical obligation on data generators to provide children and parents with the opportunity to share genomic and clinical information with researchers. Although KIDS guidelines are not legally binding, they could inform policy development worldwide.
To advance the sharing culture, along with the NIH, pediatric cancer foundations such as the St. Baldrick’s Foundation and Alex’s Lemonade Stand Foundation have incorporated genomic data-sharing requirements into their grants processes. Researchers and clinicians around the world have created dozens of pediatric cancer genomic databases and portals, but pulling these together into a larger network is problematic, especially for patients with data at more than one institution, as patient identifiers are stripped from shared data. However, initiatives like the Children’s Oncology Group’s Project Every Child and the European Network for Cancer Research in Children and Adolescents’ Unified Patient Identity may resolve this issue.
We urge the creators of pediatric cancer genomic resources to collaborate and build a real-time federated data-sharing system, and hope that the new U.S. initiative will inspire other countries to link databases rather than just create new siloed regional resources. The great advances in information technology and life sciences in the last decades have given us a new opportunity to save our children from the scourge of cancer. We must resolve to use them.
2012pharmaceutical
Amandeep Kaur
Aashir Awan, Phd
Abhisar Anand
Adina Hazan
Alan F. Kaul, PharmD., MS, MBA, FCCP
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apreconasia
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David Orchard-Webb, PhD
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Demet Sag, Ph.D., CRA, GCP
Daniel Menzin
Dror Nir
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