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Archive for the ‘Intelligent Information Systems’ Category


Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence: Realizing Precision Medicine One Patient at a Time

Reporter: Stephen J Williams, PhD @StephenJWillia2

The impact of Machine Learning (ML) and Artificial Intelligence (AI) during the last decade has been tremendous. With the rise of infobesity, ML/AI is evolving to an essential capability to help mine the sheer volume of patient genomics, omics, sensor/wearables and real-world data, and unravel the knot of healthcare’s most complex questions.

Despite the advancements in technology, organizations struggle to prioritize and implement ML/AI to achieve the anticipated value, whilst managing the disruption that comes with it. In this session, panelists will discuss ML/AI implementation and adoption strategies that work. Panelists will draw upon their experiences as they share their success stories, discuss how to implement digital diagnostics, track disease progression and treatment, and increase commercial value and ROI compared against traditional approaches.

  • most of trials which are done are still in training AI/ML algorithms with training data sets.  The best results however have been about 80% accuracy in training sets.  Needs to improve
  • All data sets can be biased.  For example a professor was looking at heartrate using a IR detector on a wearable but it wound up that different types of skin would generate a different signal to the detector so training sets maybe population biases (you are getting data from one group)
  • clinical grade equipment actually haven’t been trained on a large set like commercial versions of wearables, Commercial grade is tested on a larger study population.  This can affect the AI/ML algorithms.
  • Regulations:  The regulatory bodies responsible is up to debate.  Whether FDA or FTC is responsible for AI/ML in healtcare and healthcare tech and IT is not fully decided yet.  We don’t have the guidances for these new technologies
  • some rules: never use your own encryption always use industry standards especially when getting personal data from wearables.  One hospital corrupted their system because their computer system was not up to date and could not protect against a virus transmitted by a wearable.
  • pharma companies understand they need to increase value of their products so very interested in how AI/ML can be used.

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

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Seven Alternative Designs to Quantum Computing Platform – The Race by IBM, Google, Microsoft, and Others

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Business Bets on a Quantum Leap

Quantum computing could help companies address problems as huge as supply chains and climate change. Here’s how IBM, Google, Microsoft, and others are racing to bring the tech from theory to practice.
May 21, 2019

quantum computer at IonQ, an Alphabet-backed startup

A version of this article appears in the June 2019 issue of Fortune with the headline “The Race for Quantum Domination.”

Medicine

One day, your health may depend on a quantum leap.

  • Pharmaceutical giant Biogen teamed up with consultancy Accenture and startup 1QBit on a quantum computing experiment in 2017 aimed at molecular modeling, one of the more complex disciplines in medicine. The goal: finding candidate drugs to treat neurodegenerative diseases.
  • Microsoft is collaborating with Case Western Reserve University to improve the accuracy of MRI machines, which help detect cancer, using so-called quantum-inspired algorithms.

 

7 ways to win the quantum race

There are multiple ways that quantum computing could work.

Here’s a guide to which companies are backing which tech.

Superconducting uses an electrical current, flowing through special semiconductor chips cooled to near absolute zero, to produce computational “qubits.” Google, IBM, and Intel are pursuing this approach, which has so far been the front-runner.

Ion trap relies on charged atoms that are manipulated by lasers in a vacuum, which helps to reduce noisy interference that can contribute to errors. Industrial giant Honeywell is betting on this technique. So is IonQ, a startup with backing from Alphabet.

Neutral Atom Similar to the ion-trap method, except it uses, you guessed it, neutral atoms. Physicist Mikhail Lukin’s lab at Harvard is a pioneer.

Annealing designed to find the lowest-energy (and therefore speediest) solutions to math problems. Canadian firm D-Wave has sold multimillion-dollar machines based on the idea to Google and NASA. They’re fast, but skeptics question whether they qualify as “quantum.”

Silicon spin uses single electrons trapped in transistors. Intel is hedging its bets between the more mature superconducting qubits and this younger, equally semiconductor-friendly method.

Topological uses exotic, highly stable quasi-particles called “anyons.” Microsoft deems this unproven moonshot as the best candidate in the long run, though the company has yet to produce a single one.

Photonics uses light particles sent through special silicon chips. The particles interact with one another very little (good), but can scatter and disappear (bad). Three-year-old stealth startup Psi Quantum is tinkering away on this idea.

SOURCE

http://fortune.com/longform/business-quantum-computing/

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/01/16/rd-for-artificial-intelligence-tools-applications-googles-research-efforts-in-2018/

 

  • LIVE Day Two – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 9, 2019

www.worldmedicalinnovation.org

https://pharmaceuticalintelligence.com/2019/04/09/live-day-two-world-medical-innovation-forum-artificial-intelligence-boston-ma-usa-monday-april-9-2019/

 

  • Research and Development (R&D) Expenditure by Country represent time, capital, and effort being put into researching and designing the products of the future – Data from the UNESCO Institute for Statistics adjusted for purchasing-power parity (PPP).

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/05/26/research-and-development-rd-expenditure-by-country-represent-time-capital-and-effort-being-put-into-researching-and-designing-the-products-of-the-future-data-from-the-unesco-institute-for-s/

 

  • Resources on Artificial Intelligence in Health Care and in Medicine: Articles of Note at PharmaceuticalIntelligence.com @AVIVA1950 @pharma_BI

https://www.linkedin.com/pulse/resources-artificial-intelligence-health-care-note-lev-ari-phd-rn/

 

  • IBM’s Watson Health division – How will the Future look like?I

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/24/ibms-watson-health-division-how-will-the-future-look-like/

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals


Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

Announcement

Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME

@pharma_BI

@AVIVA1950

#KIsymposium

AGENDA

https://custom.cvent.com/C09E632203614BC295DA40B01F45218F/files/47a27352fc1f4eb486236a34203f6396.pdf

https://ki.mit.edu/news/symposium/2019

 

8:00–9:00 Coffee Break Registration

 

9:00 –9:10 Introductory Remarks Tyler Jacks Koch Institute, MIT Phillip Sharp Koch Institute, MIT

  • INTERDISCIPLINARY research at the interface of Cancer with other disciplines, this year CS, ML,

9:10–10:30 Machine Learning in Cancer Research: The Need and the Opportunity

Moderator: Phillip Sharp, Nobel Laureate 

  • ML and cancer @MIT over a decade under discussion, ML & Cancer in research is embryonic
  • REENGINEERING Healthcare using ML: understand disease, treating it
  • MIT Stephen A. Schwarzman College of Computing, AI and Healthcare – an MIT commitment of a $MILLION
  • COST OF DRUGS IS A THIRD IN TREATMENT — TOO HIGH,

James P. Allison MD Anderson Cancer Center, Nobel Laureate FOR CURE OF CANCER, THE ONLY ONE

Immune checkpoint blockade in cancer therapy: Historical perspective, new opportunities, and prospects for cures

  • long survival on ipilimumab – Lung cancer pleural effusion
  • CTLA-4Blockade – attenuated or Terminate Proliferation vs unrestrained Proliferation – APC
  • PD-1 – Ipi/Nivo vs. Ipi in metastatic Melanoma – 10 years survival after initial three years
  • FDA Approved CTLA-4 and Anti-PD-1 for a dozen indication
  • CD4, CD8, NKT – CHeckpoint blockage modulates MC38 infiltrating T cell population frequency
  • Cellular mechanism interaction
  • T cell differentiation is complex – negative costimulation regulate T cell differentiation
  • comprehensive profiling of peripheral T cell arising
  • Expansion of phynotype – distinct gene expression
  • Transcription loss of PD-1 expands CD8 T cells
  • Nuanced Model – T cell differentiation
  • Role of T cells in Immunotherapy

Aviv Regev Broad Institute; Koch Institute, MIT

Cell atlases as roadmaps to cancer

  • Experimental data and computation data
  • Tumors: a complex cellular ecosystem
  • Genetic pathology, Combination therapy Genetic variants and
  • Components vs Programs (regulatory, pathways, tissue modules, disease road map)
  • ML Applications: Immunotherapy in melanoma
  • Learning an single cell profiling and spatial organization – massively parallel cell profiling
  • malignant cells from T cell cold tumors
  • T cell T cell CD8 Malignant: Resistant
  • Computational search predicts CDK4/6 as program regulators
  • the resistance program is reversed abemaciclib sensitized to B16
  • Resistance to immunotherapy in Melanoma
  • Generalize across types of cancers – Resistance to treatment
  • Atlas for Synovial sarcoma has anti-tumor activity in its immune environment
  • Melanoma vs Synovial sarcoma (SS18-SSX fusion directly controls the core oncogenic program
  • Target the core oncogenic program therapeutically
  • IFN from CD8 T NF repress the program
  • Insi2Vec: Define a cell by intrinsic and extrinsic and spatial features: generalize across patient samples in melanoma
  • Identifying key patio-molecular archetypes
  • Expanding to full Transcriptome
  • Emerging toolbox for spatial genomics: Protein, RNA,
  • Compressed sensing,

Regina Barzilay MIT Computer Science and Artificial Intelligence Laboratory; Koch Institute,

MIT How machine learning changes cancer research

  • ML & Clinical Oncology
  • Data Science perspective – Raw Data to Cure: Extract information
  • NLP – penetration of deepLearning @MGH
  • Drug discovery – New molecules
  • MIT – ML in drug discovery
  • Risk models: age, family history, prior breast procedures breast density
  • High risk and Breast density
  • Myth #1: DEMOCRATIZATION OF AI – deep learning and customization
  • Myth @2: Bias – Deep Learning allows across population studies
  • Myth #3: Black-Box Architecture 0 @MIT Interpretable models
  • J-Clinic @MIT – ML and Healthcare Hospitals are recruited to use the algorithms

11:00–12:25 Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer

Moderator: Matthew Vander

Heiden Michael Angelo Stanford University

Comprehensive enumeration of immune cells in solid tumors using Multiplexed ion beam imaging (MIBI)

  • Tissue imaging – high dimensional, multiplexing: high sensitivity, dynamic range,
  • GI Tissue selection – Immunofluorescence (IF)  fluorophores – elemental reporters and mass spectrometry  Label antibody,
  • Immune populations in triple negative breast cancer
  • Patient stratification survival
  • mixed tumor: Immune cells, non immune cells – response to PD-1
  • Single cell: imaging data trained by nuclear interior ( nuclei only, , segmentation channel using DeepCell v1.0
  • TB granulomas,
  • Cell type identification with deep learning – No downstream
  • 3D Segmentation Melanoma
  • Assays and Ion sources

Olga Troyanskaya Princeton University

Decoding cancer genomes with deep learning

  • genome to phenotype – BIG genomics data to understand human disease
  • exosome – non coding region
  • gene regulation affected by genome for gene expression
  • sequence specific
  • Interpreting a genome: predicting the effect of any mutation: coding variant vs non-coding variance – regulatory code, transcription marks, DeepSEA – model learns from one genome interactions of biochemical level models, variants motives cell type
  • Interpreting a Genome: predicting the effect of any mutation: Noncoding variant effect in ASD from WGS data
  • DeNovo mutations: How impactful are these mutations in Autism patients – to % not explained by sequencing
  • DeepSEA identifies significant noncoding regulatory mutation burden in ASD
  • Cell-type specific expression models capture tissue-specific
  • ExPecto-prioritized putative casual GWAS variants
  • HumanBase Networks best model selected are

Dana Pe’er Sloan Kettering Institute

Manifolds underlying plasticity in development, regeneration and cancer

  • Asynchronous Nature of Cells
  • Single cell transitional states, homeostatic
  • Representing population structure as a neighbor graph: each node is a cell connected to a neighborhood – diffusion components Magic: Using structure for data recovery
  • PALANTIR: a probabilistic model for cell fate
  • Learn differentiation trajectories/pseudo-time ordering
  • Computing Differentiation probabilities stochastic process – branch
  • The gut tube comprises cells of two distinct regions
  • mammal cell plasticity – endodermal organ identities in space
  • VE cells express key organ lineage regulators similarly to DE cell
  • Uncovering unappreciated lineage relationship
  • Lung adenocarcinoma – tumor – 10% are cancer cell the rest are not therefore – they are ORGANS
  • Primary tumor exhibit epithelial progenitor cells
  • Pancreatic Ductal Adenocarcinoma – ductal Metaplasia
  • Immune ecosystem reprogrammed
  • Leptomeningeal niche — iron binding – adopt environment and prevent iron from the immune system
  • Tumor plasticity – Mutation Kras + injury to cause pancreatic cancer

 

Peter Sorger Harvard Medical School

Measuring and modeling variability in drug response in cells, tissues and clinical trials

  • combination therapy and drug synergy
  • Re-digitalization of 300 clinical trials
  • combination immunotherapy: Assumption each patient benefits from both = synergy
  • vs the assumption of NO interaction: each Patient benefit from ONE or the Other
  • Novartis PDX models
  • Gastric Cancer PDXs
  • Three way combination drug therapy – a deeper look at RAF and MEK inhibitors in BRAF mutant melanoma
  • Pharmacology of ‘reconstituted pulses” independent action
  • Heterogeneous response – sophisticated stratification of patients Four biopsy vs Three Biopsy
  • Highly multiplexed, high resolution image of FFPE specimens
  • Assaying the effects of multiple possible
  • Cancer Precision Medicine per NCI
  • Precision medicine based on therapeutic response at sisngel cell level
  • Pharmacological synergy is over emphsized – time sensitivity prevent the interactions expected

12:30–2:00 Lunch Break

2:00–3:00 Big Data, Computation and the Future of Health Care

Moderator: Susan Hockfield

Jay Bradner Novartis Institutes for BioMedical Research

  • AI is a Tool in all phases of drug discovery integrated analysis of epigenetics
  • strategy for investment in 400 Scientists, big data
  • monitoring of Clinical Trial around the  World
  • generative chemistry – 100 years of discovery in chemistry
  • validation models – bring trials to patients – distributed model of participation
  • catalyst – ML in biological experimentation
  • Published 700 articles per year at Novartis by 400 scientists
  • Population Data sets
  • IN 5 YEARS MASSIVE data will dominate molecule selection in Drug delivery

Aine Hanly VP Amgen

  • 17% improvement in survival rate in 50 years
  • Data infrastructure investment paid off
  • Partnerships, interoperability in EMR

Clifford Hudis American Society of Clinical Oncology (ASCO)

  • 75% of communication is by FAX still
  • White cell data count – convert data structure on EMR
  • Data in the Cloud for sharing
  • 165 Million in US — employer covers health cost
  • ACCESS to 2,000 clinical trials for repurposing

Constance Lehman Massachusetts General Hospital

  • Pathway – Screening mammograms is run via the algorithm, report generated, Radiologist accept or reject the mobile presented
  • more domains need AI and specialties of Medicine
  • Development and implementation then dissemination to multiple sites
  • In 1000 mammogram only 8 cancers detected

David Schenkein GOOGLE Venture (GV)

  • Regeneron, Geisinger Health System, Amgen – Big data repositories are built
  • Clinical Trials and big data
  • delivery side: AI: Pathology and Radiology, early diagnostics:
  • AI in MDs Offices to record the entire sessions
  • diagnostics – Foundation Medicine diagnostics had difficult time to prove improvement to justify reimbursement

 

3:00–3:15 Break

 

3:15–4:40 Machine Learning into the Clinic

 

Moderator: Sangeeta Bhatia, Koch Institute, MIT

Tommi S. Jaakkola MIT Computer Science and Artificial Intelligence Laboratory

Machine learning for drug design

  • Automation arises from data
  • How much data is enough? assay size vs all spectrum
  • MIT Consortium on Drug Discovery: 13 Pharma, chemistry Department, Chemical Engineering Department, CSMIT Consortium on Pharmaceutical Discovery
  • multi-resolution representations
  • substructures with precision attachment – what atoms are underlined
  • predicting molecular properties
  • programmatic optimization – machine translation problem in analogy – learn the mapping principles from examples
  • Example comparisons – Programmatic optimization – Success %
  • Understanding: Accuracy- interpretable, statistical constraints inserted
  • integrations with biology and cancer research
  • Automating Drug Design – learn from exampl

Andrew Beck PathAI

Artificial intelligence for pathology: From discovery to AI-powered companion diagnostics

  • Pathologic diagnosis informs all subsequent medical decisions
  • Discordance among pathologists is common in interpretation of breast biopsy
  • Discordance among pathologists is common in interpretation of skin biopsy – melanoma
  • Discordance among pathologists is common in interpretation across all fields: NSCLC, bladder,
  • AI – deep learning is driving major advances in computer vision
  • Reduce errors training set vs validation set
  • computer vision vs Human eye – machine learning is BETTER
  • Labeled and counting data standardization requires work with the community of Pathologists
  • Automatic interpretation of cells and tissues
  • Immuno-oncology
  • TisSue and cellular phenologies – Pathological phenotypes
  • BMS – CD8 continuum – Phenotype : Identify stromal and parenchymal CD8 infalmmation
  • AI project with Novartis

Brian Wolpin Dana-Farber Cancer Institute

Early detection of pancreatic cancer

  • ML for Pancreatic Cancer Early detection
  • Metabolic alterations: Muscle wasting, subcutaneous adipose
  • Automated Peripheral Tissue Segmentation
  • 30,000 scans: changes by race, age, : Image-Based Risk Modeling within Large Health Systems

longitudinal Cohorts: Risk Factors

  • Diabetes related to pancreatic cancer
  • risk for weight and Diabetes
  • Medication: Start and Stopped: NSAIDs, Tylenol, H2 Blocker, PPI, anti- HTN,
  • ML – early detection: altered systemic metabolism

Stephen Friend University of Oxford

From complex to complicated problems: What are the known unknowns, and unknown unknowns in using wearables to forecast symptom transitions

  • Pattern recognition for prognosis NEJM 20 years took since Breast cancer started to benefit from pattern recognition
  • Participants-Centered
  • 10M smart watches
  • APPLE – many studies Smart watch – 15,000 participants donate data via their watch – large scale ALERT based on data analysis
  • Inter-individual Diversity
  • Personal Health Assistant
  • all day sensing & recording
  • Health Assessment – signals to Symptoms
  • Deep Learning – multiple stages of non-linear feature transformation
  • Vector Institute – data analysis was done [Pregnancy]
  • Forecasting – binary labels: Well or Sick, intra and inter-individual longitudinal
  • 4YouandMe  – Foundation
  • Wearables for Cancer patients

4:40–4:45 Closing Remarks Phillip Sharp Koch Institute, MIT

 

 

Machine Learning and Cancer

The 18th Annual Koch Institute Summer Symposium on June 14, 2019 at MIT’s Kresge Auditorium will focus on Machine Learning and Cancer.

Both fields are undergoing dramatic changes, and their integration holds great promise for cancer research, diagnostics, and therapeutics. Cancer treatment and research have advanced rapidly with an increasing reliance on data-driven decisions. The volume, complexity, and diversity of research and clinical data—from genomics and single-cell molecular and image-based profiles to histopathology, clinical imaging, and medical records—far surpasses the capacity of individual scientists and physicians. However, they offer a remarkable opportunity to new approaches for data science and machine learning to provide holistic and intelligible interpretations to trained experts and patients alike. These advances will make it possible to provide far better diagnostics, discover possible chemical pathways for de novo synthesis of therapeutic compounds, predict accurately the risk of individuals for development of specific cancers years before metastatic spread, and determine the combination of agents that will stimulate immune rejection of a tumor or selectively induce the death of all cells in a tumor.

The symposium will address these issues through three sessions:

  • Machine Learning in Cancer Research: the Need and the Opportunity
  • Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer
  • Machine Learning into the Clinic

Sessions will be followed by a panel discussion of broadly informed experts moderated by MIT President Emerita Susan Hockfield.

Introductory remarks will be given by symposium co-chairs and Koch Institute faculty members Regina Barzilay, Aviv Regev and Phillip Sharp.

 

Keynote Speakers | Machine Learning in Cancer Research: the Need and the Opportunity

James P. Allison, PhD

MD Anderson Cancer Center

Regina Barzilay, PhD

MIT Computer Science and Artificial Intelligence Lab, Koch Institute for Integrative Cancer Research at MIT

Aviv Regev, PhD

Broad Institute, Koch Institute for Integrative Cancer Research at MIT

 

Session Speakers

Michael R. Angelo, MD, PhD

Stanford Unviersity

Andrew Beck

PathAI

Stephen H. Friend, MD, PhD

Sage Bionetworks

Tommi Jaakkola, PhD

MIT Computer Science and Artificial Intelligence Lab

Dana Pe’er, PhD

Memorial Sloan Kettering Cancer Center

Peter Sorger, PhD

Harvard Medical School

Olga Troyanskaya, PhD

Princeton University

Brian Wolpin, MD

Dana-Farber Cancer Institute

 

Panel Discussion | Big Data, Computation and the Future of Health Care

James (Jay) Bradner, MD

Novartis

Clifford A. Hudis, MD

American Society of Clinical Oncology

Constance D. Lehman, MD, PhD

Massachusetts General Hospital

Norman (Ned) Sharpless, MD

National Cancer Institute

 

Moderator: Susan Hockfield, PhD

Koch Institute for Integrative Cancer Research at MIT

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SOURCE

From: 2019 Koch Institute Symposium <ki-events@mit.edu>

Reply-To: <ki-events@mit.edu>

Date: Tuesday, March 12, 2019 at 11:30 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Invitation to the 2019 Koch Institute Symposium – Machine Learning and Cancer

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TALK ANNOUNCEMENT from Boston Chapter of ASA

 

Reporter: Aviva Lev-Ari, PhD, RN

 

From: Tom Lane [mailto:Tom.Lane@mathworks.com]
Sent: Tuesday, January 15, 2019 12:59 PM
To: Tom Lane
Subject: [BCASA] INFORMS talk “Impact of Bots on Opinions in Social Networks” Wed Feb 6 at MITRE in Bedford

 

Upcoming event from INFORMS, kindly shared with BCASA members:

 

Please join us for the first INFORMS BC talk of 2019 on the Impact of Bots on Opinions in Social Networks by Professor Tauhid Zaman 

·         1.  Please join us for the first INFORMS BC talk of 2019 on the Impact of Bots on Opinions in Social Networks by Professor Tauhid Zaman

Daniel Rice

 

RSVP to lservi@mitre.org by Monday, February 4th, 2018

MITRE is asking that if you plan to attend please RSVP by sending an email to lservi@mitre.org indicating your 1) name, 2) email, (3) company or university, (4) whether you are a US citizen and, if not, country of citizenship.


Date: Wednesday Feb 6, 2019, at 6:30 PM

Location:
The MITRE Corporation
M Building, 202 Burlington Road
Bedford, MA

Title: The Impact of Bots on Opinions in Social Networks

Speaker: Tauhid Zaman

Abstract:
We present an analysis of the impact of automated accounts, or bots, on opinions in a social network. We model the opinions using a variant of the famous DeGroot model, which connects opinions with network structure. We find a nontrivial correlation between opinions based on this network model and based on the content of tweets of Twitter users discussing the 2016 U.S. presidential election between Hillary Clinton and Donald Trump, providing evidence supporting the validity of the model. We then utilize the network model to predict what the opinions would have been if the network did not contain any bots which may be trying to manipulate opinions. Using a bot detection algorithm, we identify bot accounts which comprise less than 1% of the network. By analyzing the bot posts, we find that there are twice as many bots supporting Donald Trump as there are supporting Hillary Clinton.  We remove the bots from the network and recalculate the opinions using the network model. We find that the bots produce a significant shift in the opinions, with the Clinton bots producing almost twice as large a change as the Trump bots, despite being fewer in number. Analysis of the bot behavior reveals that the large shift is due to the fact that the bots post one hundred times more frequently than humans.  The asymmetry in the opinion shift is due to the fact that the Clinton bots post 50% more frequently than the Trump bots.  Our results suggest a small number of highly active bots in a social network can have a disproportionate impact on opinions.

Bio: Tauhid is an Associate Professor of Operations Management at the MIT Sloan School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT.  His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods.  Some of the topics he studies in the social networks space include predicting the popularity of content, finding online extremists, and geo-locating users.  His broader interests cover data driven approaches to investing in startup companies, non-traditional choice modeling, algorithmic sports betting, and biometric data.  His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.

 

SOURCE

From: “Tom Lane (ASA)” <Tom.Lane@mathworks.com>

Date: Tuesday, February 5, 2019 at 9:41 AM

To: “Tom Lane (ASA)” <Tom.Lane@mathworks.com>

Subject: [BCASA] INFORMS talk “Impact of Bots on Opinions in Social Networks”Wed Feb 6 at MITRE in Bedford

 

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Graph Database Market Update 2019 – Cambridge Semantics’ AnzoGraph Graph Database product Awarded Highest Rating

Reporter: Aviva Lev-Ari, PhD, RN

 

Cambridge Semantics, the leading provider of big data management and enterprise analytics software, announced that its AnzoGraph Graph Database product was rated highest in analytic processing capabilities in the latest Graph Database Market Update 2019 by Bloor Research.

 

Appended and attached is the press release for your reference.

 

Regards

Linda Sekhar
+949-872- 8631

Global Results Communications | GRC

 

                                                 

 

 

 

Cambridge Semantics Awarded Highest Rating for Analytic Processing Environments in the Graph Database Market Update 2019 by Bloor Research

 

AnzoGraph Graph Database Named #1 in Several Categories of Research

 

Boston—January 23, 2019— Cambridge Semantics, the leading provider of big data management and enterprise analytics software, announced that its AnzoGraph Graph Database product was rated highest in analytic processing capabilities in the latest Graph Database Market Update 2019 by Bloor Research.

 

The Bloor Graph Database Market Update 2019 compares property graph with RDF databases, native versus non-native implementations, single versus multi-model databases and operational versus analytic solutions. It discusses the latest trends in this market, along with an assessment of the leading vendors in the market and captures any market shift in the last two years. Relative to shifts in the graph database market, Bloor noted that Cambridge Semantics unbundled AnzoGraph from its Anzo Data Lake offering, and that Amazon and SAP also entered the graph database market.

 

Within the report, Cambridge Semantics was ranked number one in several categories including:

       Integration- Analytic environments

       Language- Analytic environments

       Features- Analytic environments

 

“There are relatively few vendors in the graph database market that have the performance to focus specifically on analytics. Cambridge Semantics not only does this with AnzoGraph, but does so without requiring (very expensive) hardware acceleration or a proprietary language,” said Philip Howard, research director at Bloor Research.

 

‘’It is an honor to be named a leading vendor for analytics in the Graph Database market 2019,” said Alok Prasad, president of Cambridge Semantics.  “We have witnessed a surging interest in graph databases to conduct interactive analytics to get better insights. Last year, we were very excited to spin-out AnzoGraph, our graph analytics database for hyper-fast performance at Big Data scale. We are seeing very strong interest in this new offering.’’

 

AnzoGraph is a massively parallel distributed native graph analytics database built to interactively analyze trillions of relationships at record speed.  The database provides BI-style analytics, graph algorithms and inferencing with open W3C standards based graph technology and labelled property graphs.  The underlying technology is a third-generation data analytics engine built by the engineers who built Netezza and the technology behind Amazon Redshift. AnzoGraph has been in production at large enterprise customers as a part of Anzo and is now available behind the firewall or in the cloud on AWS, Google Cloud Platform, Microsoft Azure and all cloud environments supporting Docker.

 

About Bloor Research

Bloor Research is a global, independent research and analyst house, focused on the idea that Evolution is Essential to business success and ultimately survival. For nearly 30 years, we have enabled businesses to understand the potential offered by technology and choose the optimal solutions for their needs.

 

About Cambridge Semantics

Cambridge Semantics Inc., The Smart Data Company®, is a big data management and enterprise analytics software company that offers a universal semantic layer to connect and bring meaning to all enterprise data. The company offers two award winning products: Anzo for Enterprise Knowledge Graphs and integrated analytics and AnzoGraph, a graph analytics database.

 

Cambridge Semantics is based in Boston, Massachusetts.

For more information visit www.cambridgesemantics.com or follow us on Facebook, LinkedIn and Twitter: @CamSemantics.

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Media Contact:

Lora Wilson/Valerie Christopherson

Global Results Communications for Cambridge Semantics

cambridge@globalresultspr.com

+1 (949) 608-0276

 

SOURCE

From: Linda Sekhar <LSekhar@globalresultspr.com>

Date: Wednesday, January 23, 2019 at 11:28 AM

Subject: Press release: Cambridge Semantics Awarded Highest Rating for Analytic Processing Environments in the Graph Database Market Update 2019 by Bloor Research

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Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

 

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 


  • worldwide oncology related care to increase by 40% in 2020
  • big movement to participatory care: moving decision making to the patient. Need for information
  • cost components focused on clinical action
  • use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

  1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

  • innovations in data depth and breadth
  • need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

  • technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
  • NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
  • data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
  • technologies are moving to data support
  • evidence will need to be tied to umbrella value propositions
  • Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

  • increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
  • Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

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