Archive for the ‘Intelligent Information Systems’ Category

MinneBOS 2019, Field Guide to Data Science & Emerging Tech in the Boston Community

August 22, 2019, 8AM to 5PM at Boston University Questrom School of Business, 595 Commonwealth Avenue, Boston, MA



MinneBOS – Boston’s Field Guide to Data Science & Emerging Tech


Leaders in Pharmaceutical Business Intelligence (LPBI) Group


REAL TIME Press Coverage for



 Aviva Lev-Ari, PhD, RN

Director & Founder, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Editor-in-Chief, Open Access Online Scientific Journal, http://pharmaceuticalintelligence.com

Editor-in-Chief, BioMed e-Series, 16 Volumes in Medicine, https://pharmaceuticalintelligence.com/biomed-e-books/





Logo, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Our BioMed e-series






Thursday, August 22 • 9:30am – 10:15am
Histopathological images are the gold standard tool for cancer diagnosis, whose interpretation requires manual inspection by expert pathologists. This process is time-consuming for the patients and subject to human error. Recent advances in deep learning models, particularly convolutional neural networks, combined with big databases of patient histopathology images will pave the path for cancer researchers to create more accurate guiding tools for pathologists. In this talk, I will review the latest advances of big data in healthcare analytics and focus on deep learning applications in cancer research. Targeted at a general audience, I will provide a high-level overview of technical concepts in deep learning image analysis, and describe a typical cloud-based workflow for tackling such big data problems. I will conclude my talk by sharing some of our most recent results based on a wide range of cancer types.


avatar for Mohammad Soltanieh-ha, PhD

Mohammad Soltanieh-ha, PhD

Clinical Assistant Professor, Boston University – Questrom
Mohammad is a faculty at Boston University, Questrom School of Business, where he teaches data analytics and big data to master’s students. Mohammad’s current research area involves deep learning and its applications in cancer research.



Thursday, August 22 • 10:30am – 11:00am

Deep learning image recognition and classification models for fashion items

Large scale image recognition and classification is an interesting and challenging problem. This case study uses fashion-MNIST dataset that involves 60000 training images and 10000 testing images. Several popular deep learning models are explored in this study to arrive at a suitable model with high accuracy. Although convolutional neural networks have emerged as a gold-standard for image recognition and classification problems due to speed and accuracy advantages, arriving at an optimal model and making several choices at the time of specifying model architecture, is still a challenging task. This case study provides the best practices and interesting insights.


avatar for Bharatendra Rai

Bharatendra Rai

Professor, UMass Dartmouth
Bharatendra Rai, Ph.D. is Professor of Business Analytics in the Charlton College of Business at UMass Dartmouth. His research interests include machine learning & deep learning applications.
  • Train data: 60,000
  • Test data: 10,000
  • Dataset available from Google MNIST Fashion Data – items in DB: data already labelled
  • Label and Description
  • Architecture: Input >> Conv >> Conv >> Pooling >> Dropout << Dense <<Flatten << Dropout >> Output
  • CNN vs Fully connected: 320 parameters: 3x3x1x32 + [32 BIAS TERM] = 320 vs
  • fully connected network parameters is 16 million
  • Train the model: 15 iterations – Training and Validation
  • Actual vs Predicted: 94% was classified correctly = Accuracy: 94% 5974 vs 4700 (78%)
  • Confusion Matrix – Test 720 correctly classified for item 6  – Probability va Actual Vs Predicted
  • Image generation: Noise . gnerator Network > fake Image vs Real image – GAN Loss va Discriminator Loss
  • CNN network help reduce # of parameter
  • Droppot layers can help reduce overfitting
  • validation split of x%chooses last x% of train data
  • Generation of new data is challenging



Thursday, August 22 • 11:15am – 12:00pm

Rapid Data Science

Most companies today require fast, traceable, and actionable answers to their data questions. This talk will present the structure of the data science process along with cutting edge developments in computing and data science technology (DST) with direct applications to real world problems (with a lot of pictures!). Everything from modeling to team building will be discussed, with clear business applications.


avatar for Erez Kaminski

Erez Kaminski

Leaders Global Operations Fellow, MIT
Erez has spent his career helping companies solve problems using data science. He is currently a graduate student in computer science and business at MIT. Previously, he worked in data science at Amgen Inc. and as a technologist at Wolfram Research.



Thursday, August 22 • 1:00pm – 1:45pm

Health and Healthcare Data Visualization – See how you’re doing

Health and healthcare organizations are swimming in data but few have the skills to show and see the story in their data using the best practices of data visualization. This presentation raises awareness about the research that inform these best practice and stories from the front of groups who are embracing them and re-imagining how they display their data and information. These groups include the NYC Dept of Health & Mental Hygiene, The Centers for Medicare and Medicaid (CMS), and leading medical centers and providers across the country.


avatar for Katherine Rowell

Katherine Rowell

Co-Founder & Principal, Health Data Viz
Katherine Rowell is a health, healthcare, and data visualization expert. She is Co-founder and Principal of HealthDataViz, a Boston firm that specializes in helping healthcare organizations organize, design and present visual displays of data to inform their decisions and stimulate… Read More →
  • dashboard for Hospital CEOs



Thursday, August 22 • 2:00pm – 2:45pm

AI in Healthcare

Benefits, challenges and impact of AI and Cybersecurity on medicine.


avatar for Vinit Nijhawan

Vinit Nijhawan

Lecturer, Boston University
Vinit Nijhawan is an Entrepreneur, Academic, and Board Member with a track record of success, including 4 startups in 20 years.
  • US: Spends the most on Health Care (HC) death per 100K people is the highest
  • Eric Topol – Diagnosis is not done correctly, AI will help with diagnosis
  • Diagnosis — AI will have the most impact; VIRAL infections are diagnosed as bacterial infections and get antibiotics for treatment
  • Image Classification my ML – decline below to human misclassification
  • Training Data sets – Big data
  • Algorithms getting better
  • Data Capture getting better – HC as well
  • Investment in HC is the greatest
  • SECURITY related to Implentable Medical Devices = security attacks – hacking and sending signal to implentable devices




Thursday, August 22 • 3:00pm – 3:30pm

Patient centric AI: Saving lives with ML driven hospital interventions

This presentation will cover the use of machine learning for maximizing the impact of a hospital readmissions intervention program. With machine learning, clinical care teams can identify and focus their intervention efforts on patients with the highest risk of readmission. The talk will go over the goals, logistics, and considerations for defining, implementing, and measuring our ML driven intervention program. While covering some technical details, this presentation will focus on the business implementation of advanced technology for helping people live healthier lives.


avatar for Miguel Martinez

Miguel Martinez

Data Scientist, Optum
Miguel Martinez is a Data Scientist at Optum Enterprise Analytics. Relied on as a tech lead in advancing AI healthcare initiatives, he is passionate about identifying and developing data science solutions for the benefit of organizations and people.




Thursday, August 22 • 3:45pm – 4:15pm

Using Ontologies to Power AI Systems

There’s a great deal of confusion about the role of a knowledge architecture in artificial intelligence projects. Some people don’t believe that any reference data is necessary. But in reality reference data is required- even if there is no metadata or architecture definitions outside defined externally for an AI algorithm, someone has made the decisions about architecture and classification within the program. However, this will not work for every organization because there are terms, workflows, product attributes, and organizing principles that are unique to the organization and that need to be defined for AI tools to work most effectively.


avatar for Seth Earley

Seth Earley

CEO, Earley Information Science
Seth Earley is a published author and public speaker about artificial intelligence and information architecture. He wrote “There’s no AI without IA” which has become an industry catchphrase used by a number of people including Ginny Rometty, the CEO of IBM.
  • Ontology, taxonomies, thesauri – conceptual relationships
  • Object-Oriented Programming and Information Architecture using AI is Old wine in new bottles


Thursday, August 22


 Senior Leadership Panel: Future Directions of Analytics

This panel includes senior leaders from across industry, academia & government to discuss challenges they are tackling, needs they anticipate and goals they will achieve


avatar for Bonnie Holub, PhD

Bonnie Holub, PhD

Industry & Business Data Science, Teradata
Bonnie has a PhD in Artificial Intelligence and specializes in correlating disparate sets of Big Data for actionable results.

Read Full Post »

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:





# Hashtags

#BIO2019 (official meeting hashtag)

Read Full Post »

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.”


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.




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



  • 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



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



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

Reporter: Aviva Lev-Ari, PhD, RN


Read Full Post »

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.














Read Full Post »

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


Aviva Lev-Ari, PhD, RN,

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








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

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


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


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

[if gte mso 9]>


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

Read Full Post »

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

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

Title: The Impact of Bots on Opinions in Social Networks

Speaker: Tauhid Zaman

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.



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


Read Full Post »

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.



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.



Media Contact:

Lora Wilson/Valerie Christopherson

Global Results Communications for Cambridge Semantics


+1 (949) 608-0276



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

Read Full Post »

Older Posts »