Machine learning, specifically for drug discovery, development, diagnostics and healthcare, is highly data-intensive with disparate types of data being generated that have historically been trial-and-error processes. Deep learning, machine learning (ML) and artificial intelligence (AI), coupled with correct data, have the potential to make these processes less error-prone and increase the likelihood of success from drug discovery to the real-world setting. The Machine Learning and Artificial Intelligence program will discuss lessons learned from case studies as well as challenges that lay ahead.

Final Agenda

Monday, March 11

10:30 am Conference Program Registration Open

TRANSLATING DATA INTO THERAPIES

11:50 Chairperson’s Opening Remarks

Joel Saltz, MD, PhD, Chair and Professor Department of Biomedical Informatics, Vice President, Clinical Informatics Stony Brook Medicine, Cherith Endowed Chair, Stony Brook University


12:00 pm KEYNOTE PRESENTATION: Translating Ten Trillion Points of Data into Diagnostics, Therapies and New Insights in Health and Disease

Atul ButteAtul Butte, MD, PhD, Priscilla Chan and Mark Zuckerberg Distinguished Professor, Director, Bakar Computational Health Sciences Institute, University of California, San Francisco, Chief Data Scientist, University of California Health (UC Health)

Dr. Butte will highlight his lab’s work, including the use of publicly-available molecular measurements to find new uses for drugs, including new therapies for autoimmune diseases and cancer, discovering new druggable targets in disease, the evaluation of patients and populations presenting with whole genomes sequenced, integrating and reusing the clinical and genomic data that result from clinical trials, discovering new diagnostics including blood tests for complications during pregnancy, and how the next generation of biotech companies might even start in your garage.

12:30 Visualizing Phenotypes from High Content Screening

Henstock_PeterPeter Henstock, PhD, AI & Machine Learning Lead, Pfizer

High content screening (HCS) poses a data challenge not only by the scale of the problem, but by the variety of ways that objects that can be characterized. Traditional image processing approaches have focused on identifying cells and enumerating measurements from the images to create multidimensional feature sets. Deep learning offers opportunities to learn to recognize cell types or cellular components and quantifies them by their co-locations, counts, etc. A difficulty of both methods is providing an interpretation of the results to the scientist. In HCS where the phenotypes are unknown, the variability between treatments and cells further complicates the ability to distinguish the classes of response and quantify how they vary from one or more sets of controls. This talk will provide an example imaging workflow and highlight approaches for visualizing the results.

1:00 Session Break

1:10 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

2:10 Session Break

AI AUTOMATED DIAGNOSTICS

2:30 Chairperson’s Remarks

Joel Saltz, MD, PhD, Chair and Professor Department of Biomedical Informatics, Vice President, Clinical Informatics Stony Brook Medicine, Cherith Endowed Chair, Stony Brook University

2:40 Autonomous Detection of Diabetic Retinopathy in Frontlines of Care

Abramoff_MikeMichael D. Abràmoff, MD, PhD, Retina Service, The Robert C. Watzke, MD, Professor of Ophthalmology and Visual Sciences, Electrical and Computer Engineering, and Biomedical Engineering, Department of Ophthalmology and Visual Sciences, University of Iowa Hospital and Clinics; Founder and CEO, IDx

This talk will discuss the pivotal trial of an AI system to detect diabetic retinopathy (DR) that leads to the first FDA approval of an autonomous AI diagnostic system.


3:10 Digital Pathology – Pathomics Biomarkers and Pathology Computer Aided Detection

Saltz_JoeJoel Saltz, MD, PhD, Chair and Professor Department of Biomedical Informatics, Vice President, Clinical Informatics Stony Brook Medicine, Cherith Endowed Chair, Stony Brook University

Digital Pathology is a rapidly developing field – I will describe ongoing work to develop tools and methods that promises to improve pathology diagnostic reproducibility as well as to improve the predictive power offered by Pathology in treatment selection and outcome prediction.

3:40 Machine Learning in Imaging

Patel_AalpenAalpen A. Patel, MD, FSIR, Chair, Department of Radiology, Geisinger

Deep learning has revolutionized the field of computer vision. In medical imaging, deep learning has been used in a variety of image processing tasks and recently, for diagnostic purposes such as diabetic retinopathy and skin cancer detection. Our recent paper describes a DL-based identification of intracranial hemorrhage on head CTs and using it to prioritize the list for interpretation. Using a large clinical, heterogenous data set is very valuable in generalizing and translating to clinical tools.

4:10 Sponsored Presentation (Opportunity Available)

4:40 Refreshment Break and Transition to Plenary Session


8:00 Plenary Keynote Session

6:00 Grand Opening Reception in the Exhibit Hall with Poster Viewing

7:30 Close of Day

Tuesday, March 12

7:30 am Registration Open and Morning Coffee


8:00 Plenary Keynote Session

9:15 Refreshment Break in the Exhibit Hall with Poster Viewing

ADOPTING AND ADAPTING AI SOLUTIONS

10:15 Chairperson’s Remarks

Lucas Lochovsky, PhD, Postdoctoral Associate, Computational Science, The Jackson Laboratory for Genomic Medicine

10:25 Machine Learning: From Robotics to Information Technology Products

Laurent_PatrykPatryk Laurent, PhD, Director of Emerging Technology, Office of the CTO, DMGT plc

Machine learning has advanced significantly in robotics, computer vision, and the internet-of-things. Better hardware is now available, as are improved software frameworks. But industry performance requirements continually drive innovation and robustness improvements. In computer vision, classical deep learning approaches based on pattern recognition are being challenged by neural network methods that leverage the dynamical and interactive aspects in visual signals. Insights here are likely transferrable to many machine learning problems.

10:55 Evaluation of Algorithms from a Clinical Perspective

Kakarmath_SujaySujay Kakarmath, MD, MS, Research Fellow, Partners Connected Health Innovation, Partners HealthCare

Novel predictive algorithms developed using machine learning methods are often optimized to achieve the best area under the receiver operating characteristic curve (AUC). However, this metric is often not relevant clinically. How, then, can health professionals make conclusions about the real utility of an algorithm? The Algorithm Science team at Partners Connected Health invests a great deal of time thinking about the right questions, working out potential pitfalls and developing best practices in evaluating machine learning-based solutions.

11:25 Generative Adversarial Networks (GAN) and Its Potential Application in Generating Synthetic Data for Clinical Trial

Zhao_ShanrongShanrong Zhao, PhD, Director, Computational Biology, Pfizer

Generative Adversarial Networks (GAN) is emerging as a disruptive method in machine learning, and has been successfully applied to text, image and voice processing. GAN has many potential applications in clinical trials as well. It can be used to generate synthetic data and virtual patients for in silico trials, and accordingly, to reduce the cost related to the drug development and clinical trials.

11:55 Sponsored Presentation (Opportunity Available)

12:25 pm Session Break

12:35 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:35 Refreshment Break in the Exhibit Hall with Poster Viewing

MACHINE LEARNING FOR BIOMARKER DISCOVERY

2:05 Chairperson’s Remarks

Renee Deehan Kenney, Vice President, Computational Biology, PatientsLikeMe

2:10 (How to Fix) the Very Reasonable Ineffectiveness of Machine Learning in Biomarker Discovery

Haque_ImranImran Haque, PhD, Former CSO, Freenome

In this talk I will argue that our lack of complete understanding of biology and the time intensive method of sample collection have hindered biomarker discovery, and in particular that while the motivation for the empirical approach is correct, empirical biomarker discovery has failed to grapple with data set sizes in biology that are several orders of magnitude smaller than those in other fields where machine learning has had success. Finally, I will propose lines of research to bridge the gap between the mechanistic and empirical approaches, and in doing so address part of this data shortage.

2:40 Predicting Successful Therapeutic Targets Using Deep Learning, Matrix Factorization, and Network Propagation

Agarwal_PankajPankaj Agarwal, PhD, FRSB, Senior Fellow & Senior Director, Computational Biology, RD Target Sciences, GSK

AI and Machine Learning are being widely used in drug discovery, yet, there are significant challenges because of the lack of training examples in the biological data space. We will show three case studies examining the same problem from different angles and using different methods. You will see the limitations of each approach and how different validation schemes impact results.

3:10 Elucidating the Determinants of Preterm Birth in the Era of Precision Medicine

Sirota_MarinaMarina Sirota, PhD, Assistant Professor, Bakar Computational Health Sciences Institute, UCSF

Survival for most children born preterm has improved considerably, but surviving children remain at increased risk for a variety of serious complications, many of which contribute to lifelong challenges. The exact mechanism of spontaneous preterm birth is unknown, though a variety of social, environmental, and maternal factors have been implicated in its cause. In the Sirota Lab, we are in particular interested in applying computational integrative methods to investigate the role of the immune system in pregnancy and elucidating genetic, environmental and clinical determinants of preterm birth.

3:40 Sponsored Presentation (Opportunity Available)

4:10 St. Patrick’s Day Celebration in the Exhibit Hall with Poster Viewing

5:00 Breakout Discussions in the Exhibit Hall

6:00 Close of Day

Wednesday, March 13

7:30 am Registration Open and Morning Coffee


8:00 Plenary Keynote Session

10:00 Refreshment Break and Poster Competition Winner Announced in the Exhibit Hall


FEATURED SESSION: MACHINE LEARNING FOR PHARMA R&D

10:50 Chairperson’s Remarks

Pankaj Agarwal, PhD, FRSB, Senior Fellow & Senior Director, Computational Biology, RD Target Sciences, GSK

11:00 Machine Learning and Statistical Approaches for Reverse Translation

Sandor Szalma, PhD, Global Head, Computational Biology, Takeda Pharmaceuticals

We have recently implemented a global computational biology team and been expanding the capabilities to enable reverse translation across the diseases of our interest. In this presentation, I will discuss our computational infrastructural approach and a couple of initial computational experiments to explore real-world data and machine learning methodologies to better understand patient journeys in support of the research and development organization.

11:30 Diagnosing Rare Disease Patients: Progress in Fully Automated Diagnosis

Tom Defay, Senior Director, R&D Strategy and Alliances, SPMD, Strategy, Program Management and Data Sciences, Alexion

Diagnosing patients with rare disease is challenging. Whole exome and whole genome sequencing have improved our diagnostic abilities, but in many cases many potentially disease-causing genes have potentially pathogenic mutations associated with them. By combining phenotypic information automatically extracted from the patient’s EMR with a patient’s genome sequence, we have developed a system for proposing possible diagnoses. The effectiveness and potential utility of this approach will be discussed.

12:00 pm Projects Colossus and Enigma:  The Use of AI Methods in Early Drug Discovery and Late Drug Development

Branson_KimKim Branson, PhD, Head of A.I (ECDi), Genentech 

The historical repository of clinical trial data represents some of the most costly and important data in pharmaceutical development.   Clinical trial data is often analyzed by function, bio marker, safety, pharmacokinetic etc.  We present the development of the Colossus project at Genentech; an extensible framework integrating clinical, pathology, imaging, rna and genomic data.  Using this data we are able to build models to predict responders and non responders,  clinical trajectory and adverse event likelihood.  Using this we were able to identify an new responder subpopulation, which replicated across trials and indications.   The enigma project uses deep learning methods in the discovery and formulation of novel small molecules.  Deep learning and generative methods have produced models with greater predictive power and robust characteristics than previous methods. 

 

12:30 Session Break

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:10 Refreshment Break in the Exhibit Hall and Last Chance for Poster Viewing

MACHINE LEARNING TO MAKE BIG DATA CLINICALLY ACTIONABLE

1:50 Chairperson’s Remarks

Hua Xu, PhD, Professor, Director, Center for Computational Biomedicine, The University of Texas Health Science Center at Houston, School of Biomedical Informatics

2:00 Machine Learning for Data Driven Decision Making of Clinical Trials

Xu_HuaKevin Hua, PhD, Senior Manager, AI Machine Learning Development, Digital Health Intelligence Group, Bayer

Clinical trials are expensive business expenditures. Advances in AI/machine learning and data mining technology and availability of data make data-driven decision making possible in drug development. We would like to present a case study where wearable devices and deep learning models are used to help clinical scientists make faster and more accurate decisions during clinical trials.

2:30 Informatics Approaches to Reducing the Sanger Burden in Clinical NGS Laboratories

Lebo_MattMatthew Lebo, PhD, FACMG, Director, Bioinformatics, Partners Personalized Medicine; Instructor, Pathology, Brigham and Women’s and Harvard Medical School

Recent work has highlighted the accuracy and completeness of NGS such that these additional assays may not be required, especially in the realm of orthogonal confirmation of variants. However, many of these studies have been underpowered to accurately define thresholds for ensuring high confidence in NGS variant calling. In this talk, we’ll discuss algorithmic and machine learning approaches to tackle this problem, demonstrating the ability to dramatically reduce, but crucially not eliminate, the burden of orthogonal confirmation in germline NGS assays.

3:00 From Pixels to Phenotypes: Analysis Of Cellular Images With Multi-Scale Convolutional Neural Networks

godinez_WilliamWilliam J. Godinez, PhD, Research Investigator, Novartis Institutes for BioMedical Research (NIBR)

Large-scale cellular imaging and phenotyping is a widely adopted strategy for understanding biological systems and chemical perturbations. Quantitative analysis of cellular images for identifying phenotypic changes is a key challenge within this strategy, and has recently seen promising progress with approaches based on deep learning. In this talk we describe our approaches based on deep multi-scale convolutional neural networks for phenotyping cellular images. We discuss supervised as well as unsupervised learning strategies, with the latter requiring no phenotypic labels for training. We present an example application based on images of E. Coli bacteria to show how we use machine learning to predict the binding preferences of antibiotics directly from microscopy image data.

3:30 Session Break

DEVELOPING TOOLS IN SUPPORT OF CLINICAL DATA MINING FOR DX INNOVATION

3:40 Chairperson’s Remarks

Harry Glorikian, MBA, Healthcare Consultant; Author, MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market

3:45 Digitizing Human Health with Molecular and Phenome Data

Deehan_ReneeRenee Deehan Kenney, PhD, Vice President, Computational Biology, PatientsLikeMe

PatientsLikeMe has over a decade of experience collecting patient-generated health data to help individuals track information about their health and improve their outcomes. In order to leverage concomitant advances in molecular measurement technology, we have begun collecting and analyzing biosamples on a diverse array of omics platforms, including DNA and RNA sequencing, methylomics, immunosignature, metabolomics and proteomics measurements. In this session, we will discuss the development of a biocomputing platform that applies machine learning and other modeling techniques to aid researchers in extracting meaningful health insights from complex biological and phenomic data sets, and a case study that demonstrates the utility of the platform.

4:15 Cancer Deep Phenotype Extraction from Electronic Medical Records (DeepPhe)

Savova_GuerganaGuergana Savova, PhD, FACMI, Associate Professor, PI Natural Language Processing Lab, Boston Children’s Hospital and Harvard Medical School

We present the DeepPhe software for extracting deep phenotype information from EMRs. The software is a significant departure from other efforts in the field, as it enables comprehensive longitudinal data processing from various sources. The envisioned applications are far-reaching, from translational clinical investigations to cancer surveillance and precision oncology initiatives.

4:45 Large-Scale Medical Data Mining - The AI Revolution in Medical Care

roehrl_MikeMichael Roehrl, MD, PhD, Director, Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center

Modern precision medicine is a quantitative data-driven science. We will describe the challenges of transforming the traditionally descriptive and qualitative practice of medicine into a discipline that increasingly incorporates concepts from computer science, mathematics, engineering, and quantitative biology. One specific area of interest is the vast amount of free text based medical data. We will discuss examples from pathology and demonstrate Natural Language Processing for Big Data analytics.

5:15 Close of Conference Program