Emerging AI Technologies

AI technologies are essential to the advancement of precision medicine. There is no shortage of examples of how they can be leveraged from R&D to translational research, clinical trials, and real-world data. However, the current round of solutions can be expensive and complex to deploy and manage. The Emerging AI Technologies track will explore currently deployed use cases of AI technology, what problems have been addressed successfully, where there are some challenges, and what future technology developments to keep an eye on, such as machine vision, emotion gestures/affective computing, NLP, NLG, knowledge graphs, deep learning, reinforcement learning, quantum computing, synthetic data, and more. Hear from pharma industry use cases, as well as technology users and emerging technology solution providers. How can these emerging AI technologies move from correlation to causality analysis?

Tuesday, October 6

PLENARY KEYNOTE PROGRAM

10:00 am

Welcome Remarks

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute
Scott Parker, Director of Product Marketing, Marketing, Sinequa
10:15 am

NIH’s Strategic Vision for Data Science

Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health
Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health
11:05 am

LIVE Q&A: Session Wrap-Up Panel Discussion

Panel Moderator:
Ari E Berman, PhD, CEO, BioTeam Inc
11:25 am Lunch Break - View Our Virtual Exhibit Hall
11:55 am Recommended Pre-Conference Workshops*
W1: Data Management for Biologics: Registration and Beyond
W2: A Crash Course in AI: 0-60 in Three
W3: Data Science Driving Better Informed Decisions

*Separate registration required. See workshop page for details.

1:55 pm Refresh Break - View Our Virtual Exhibit Hall
2:15 pm Recommended Pre-Conference Workshops*
W4: Digital Biomarkers and Wearables in Pharma R&D and Clinical Trials
W5: AI-Celerating R&D: Foundational Approaches to How Emerging Technologies Can Create Value
W6: Dealing with Instrument Data at Scale: Challenges and Solutions

*Separate registration required. See workshop page for details.

4:15 pm Close of Day

Wednesday, October 7

AUGMENTED INTELLIGENCE AND MACHINE LEARNING

9:00 am

The MPrint-Open Knowledge Network: Creating a Central Portal for Molecular AI Activities

James K. Ferri, PhD, Professor, Associate Department Chair and Undergraduate Program Director, Department of Chemical and Life Science Engineering, Virginia Commonwealth University

The research objective of MPrint-OKN is to create a valuable collaborative system that develops and distributes the most advanced molecular models, machine learning, data science, and data visualization tools available to the many disciplines requiring molecular systems for product development. We seek to reduce both the cost and time of discovering and developing next-generation molecular-based applications by placing advanced tools in the hands of more researchers. This presentation will cover our development of the portal and a discussion of some of the tools and strategies we are using to centralize molecular AI activities.

9:20 am

CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data

Richard Head, MS, Professor, Genetics and Pathology & Immunology; Director, Genomics, McDonnell Genome Institute Genetics; Genome Technology Access Center at the McDonnell Genome Institute (GTAC@MGI), Washington University School of Medicine

Utilizing a revolutionary combination of contextual language processing and memory generation with components of artificial intelligence, the platform enables rapid human interpretation and hypothesis generation (Augmented Intelligence). Direct assessment of disease processes, target identification, drug mechanism of action, safety concerns, and the identification of translational mechanisms between animal models and human disease can occur in hours or days, instead of the weeks to months traditionally required to reach this depth of understanding.

9:40 am

CO-PRESENTATION: Harnessing Machine Learning to Identify Causal Drivers of Operational Success in Clinical Trials

Sylvia Marecki, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany
Omesan Nair, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany

The Operational Design Center at EMD Serono has leveraged causal machine learning to analyze hundreds of variables across thousands of clinical trials with the objective of identifying causal drivers of enrollment success. Understandings gained from these efforts allow for creating a predictive algorithm for optimizing study design to conduct faster, less expensive trials. Early insights will be shared.

10:00 am Coffee Break - View Our Virtual Exhibit Hall
10:20 am

Automated Information Extraction from Pathology Reports Using Natural Language Processing

Vishakha Sharma, PhD, Principal Data Scientist, Roche Molecular Systems Inc.

Many critical facts required by healthcare AI applications are locked in unstructured free-text data. Recent advances in deep learning have raised the bar on achievable accuracy for tasks, like named entity recognition, entity resolution, de-identification and others, using novel healthcare-specific networks and models. Dr. Sharma will discuss how Roche applies the greatest advances in AI for healthcare to extract clinical facts from pathology reports and radiology. Dr. Sharma will then detail the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale.

10:40 am

The Emergence of HL7 FHIR as a Critical Component in Data Interoperability:  Its Impact upon AI for Healthcare and Drug Discovery

Charles Jaffe, MD, PhD, Chief Executive Officer, Health Level 7

Over the past several years there has been a significant increase in the share of patient records stored electronically. Variations in the major EMR systems, the challenges of secure permissions and other factors have made secure exchange of sensitive information, particularly at scale, quite difficult. FHIR effectively addresses these issues, and facilitates data sharing to permit access from small cohorts to enormous datasets. This capability is foundational for the wider application of AI to healthcare issues, and will help provide much better access to shared healthcare datasets. Prospects for the future, ongoing challenges and promising opportunities will also be discussed.

David Milward, Dr., Senior Director, NLP Technology, Linguamatics, an IQVIA Company

AI technologies, such as natural language processing (NLP), are expanding their use of deep learning methods. We will review what NLP is, uses of Machine Learning (ML) within NLP, and use of NLP for wider ML projects in decision making. We will describe real-world use cases to improve patient outcomes.

11:30 am LIVE Q&A:

Session Wrap-Up Panel Discussion

 

Panel Moderator:
Vishakha Sharma, PhD, Principal Data Scientist, Roche Molecular Systems Inc.
Panelists:
James K. Ferri, PhD, Professor, Associate Department Chair and Undergraduate Program Director, Department of Chemical and Life Science Engineering, Virginia Commonwealth University
Charles Jaffe, MD, PhD, Chief Executive Officer, Health Level 7
Richard Head, MS, Professor, Genetics and Pathology & Immunology; Director, Genomics, McDonnell Genome Institute Genetics; Genome Technology Access Center at the McDonnell Genome Institute (GTAC@MGI), Washington University School of Medicine
Sylvia Marecki, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany
Omesan Nair, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany
Esteban Rubens, Healthcare AI Principal, NetApp
11:50 am Lunch Break - View Our Virtual Exhibit Hall
11:55 am Interactive Breakout Discussions

Consider joining a breakout discussion group. These are informal, moderated discussions with brainstorming and interactive problem solving, allowing participants from diverse backgrounds to exchange ideas and experiences and develop future collaborations around a focused topic.

Michael Riener, President, RCH Solutions

Join us for a lively discussion among prominent pharma leaders, and learn:

Why, when & how to implement a public Cloud for your computing needs

Challenges and opportunities when setting and managing stakeholder expectations

Critical keys to success to realize the best outcomes

To learn more about RCH Solutions, visit our Virtual Booth

Joe Donahue, Managing Director, Life Sciences, Accenture

Hosted by Joe Donahue, Managing Director, Life Sciences, Accenture

 

Participants include: 

Andreas Matern, Head of Digital Translational Medicine, Sanofi

John Quackenbush, Professor of Computational Biology and Bioinformatics; Harvard T.H. Chan School of Public Health

Seungtaek Lee, VP, Strategic Partnerships and AI RWE Head of CoE; ConcertAI

Preston Keller, PhD, MBA, President & CCO, PercayAI

Philip Payne, PhD, Becker Professor and Chief Data Scientist, Washington University in St. Louis

 

Jeff Evernham, VP of Customer Solutions, Consulting, Sinequa

Most large scale analysis of clinical trial data only leverages part of the picture, ignoring unstructured data and limiting findability across all the information collected throughout multiple disparate data sources.  This roundtable will discuss leveraging a cognitive platform to combine all data from multiple sources into one unified view using a single entry point to the data.

 

Sasha Paegle, Life Science Business Development, Dell Technologies

Evaluating, optimizing and benchmarking of next generation sequencing (NGS) methods are essential for clinical, commercial and academic NGS pipelines. Optimizations for speed and accuracy often require making trade-offs relative to other constraints. Join this roundtable to discuss benchmarking strategies, trade-offs, and the value of benchmarking genomics tools and applications. 

PLENARY KEYNOTE PROGRAM

Michael Schwartz, Head, Product Marketing, Marketing, Benchling

The life science industry has forged ahead with a new generation of therapeutics. A new R&D paradigm is required to develop scientific platforms, manage data complexity, and orchestrate progress across specialized teams. Digital solutions and data ecosystems are at the heart of this, but require both structure and adaptability to thrive in the modern life science R&D environment.

12:30 pm KEYNOTE PRESENTATION & PANEL DISCUSSION:

Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

Allison Proffitt, Editorial Director, Bio-IT World

While the precision medicine movement augurs for better outcomes through targeted prevention and intervention, those ambitions entail a bold new set of data challenges. Various panomic and traditional data streams must be integrated if we are to develop a comprehensive basis for individualized care. However, deriving actionable information requires complex predictive models that depend on the acquisition and integration of patient data on a massive scale. This picture is further complicated by new data streams emerging from quantified self-tracking and health social networks, both of which are driven by experimentation-feedback loops. Tackling these issues may seem insurmountable, but recent advancements in human/AI partnerships and crowdsourcing science adds a new set of capabilities to our analytic toolkit. This session describes recent work in online collective systems that combine human and machine-based information processing to solve biomedical data problems that have been otherwise intractable, and an information processing ecosystem emerging from this work that could transform the landscape of precision medicine for all stakeholders. Pietro will open with a framing talk, followed by short presentations from each panelist, ending with a moderated Q&A discussion by Allison with speakers and attendees. 

Panelists:
Seth Cooper, PhD, Assistant Professor, Khoury College of Computer Sciences, Northeastern University
Lee Lancashire, PhD, CIO, Cohen Veterans Bioscience
Pietro Michelucci, PhD, Director, Human Computation Institute
Jérôme Waldispühl, PhD, Associate Professor, School of Computer Science, McGill University
1:55 pm Refresh Break - View Our Virtual Exhibit Hall

ACCELERATING THE PACE OF R&D

2:10 pm

AI-Celerating Preclinical Research: Machine Learning, Deep Learning and Robotic Process Automation to Scale Crystallography Throughput

Brian Martin, Head of AI, R&D Information Research, Research Fellow, AbbVie

AbbVie is implementing the AbbVie CHILE (Crystallography with Human-In-Loop Enhancement) platform where machine learning models use human-in-loop feedback to improve classifications at important decision points in the process of structure identification. RPA and other automation technologies provide integration with external and internal systems to handle repetitive and directive tasks. Our work illustrates how combining the power of multiple digital technologies can help transform scientific workflows and deliver operational, analytical, and experiential value to our scientists.

2:30 pm

Advanced Imaging and AI Technologies Providing New Image and Data Analysis Challenges and Opportunities

Richard Goodwin, PhD, Director & Head, Imaging and Artificial Intelligence, Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK

AstraZeneca is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. This is enabled though the use of an extensive range of cutting-edge imaging technologies that support studies into the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies, and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis, which integrate spatial relationships in unprecedented detail.

2:50 pm Refresh Break - View Our Virtual Exhibit Hall
3:10 pm

PANEL: Framework and Approach to Unlock the Potential of Quantum Computing in Drug Discovery

Panel Moderator:
Brian Martin, Head of AI, R&D Information Research, Research Fellow, AbbVie

In 2019, major life sciences companies mobilized to form a pre-competitive, collaborative quantum computing working group (QuPharm) and delineate a framework and approach to accelerate realizing the potential of quantum acceleration in drug discovery. Learn from industry thought leaders on how to valuate and map problems into quantum algorithms, set up organizations to enable and scale quantum computing pilots and establish effective cross-industry, tech, and start-up collaborations.

Panelists:
Philipp Harbach, Head of In Silico Research, EMD Digital
Celia Merzbacher, PhD, Associate Director, Quantum Economic Development Consortium, SRI International
John Wise, Consultant, Pistoia Alliance Inc
Zahid Tharia, Consultant, Pistoia Alliance Inc
4:00 pm LIVE Q&A:

Session Wrap-Up Panel Discussion

Panel Moderator:
Brian Martin, Head of AI, R&D Information Research, Research Fellow, AbbVie
Panelists:
Richard Goodwin, PhD, Director & Head, Imaging and Artificial Intelligence, Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
Philipp Harbach, Head of In Silico Research, EMD Digital
Celia Merzbacher, PhD, Associate Director, Quantum Economic Development Consortium, SRI International
John Wise, Consultant, Pistoia Alliance Inc
Zahid Tharia, Consultant, Pistoia Alliance Inc
4:20 pm Bio-IT Connects - View Our Virtual Exhibit Hall
5:00 pm Close of Day

Thursday, October 8

FUTURE OF AI: DATA SHARING, STANDARDS, SECURITY AND PRIVACY

9:00 am

AI & Strategy Development: Collaboration, Externalisation, Data Sharing, and Integration

Nick Lynch, PhD, Founder & CTO, Curlew Research

AI is at peak hype at present, but its full potential will not be realized unless a clear and incremental strategy is adopted. In this talk, we will discuss the current state of collaboration and pre-competitive activities within AI in Life Sciences R&D, where this is heading, and how we can influence it as a community. We will discuss how data sharing and the use of data standards will need to underpin the future potential of AI and look ahead to the future approaches.

9:20 am

New Methods to Integrate and Leverage Genomic and Clinical Data to Improve Rare Disease Diagnostics

Tom Defay, Deputy Head, Diagnostics, Alexion Pharmaceuticals

Rare disease patients suffer too often from long diagnostic delays and misidentified diseases. This creates a significant burden, not just for patients, but for healthcare systems. We present in this talk examples of instances where we have collaborated with researchers and hospital systems to develop novel approaches for rare disease patient identification using tools like genomics, machine learning, and NLP.

9:40 am Coffee Break - View Our Virtual Exhibit Hall
10:00 am

Machine-Learned Molecular Models for Protein Structure, Networks, and Design

Mohammed AlQuraishi, PhD, Assistant Professor, Systems Biology, Columbia University
10:20 am

Applications of Groundbreaking AI Technology to Produce Realistic, Fully De-Identified Patient Data to Test and Validate Preclinical Modelling Methods

Kimberly Robasky, PhD, Head, Translational Science, Renaissance Computing Institute (RENCI)

Researchers use biomarker and outcomes data to model and predict adverse events. However, access restrictions to safeguard patient privacy necessarily slow down the rate of discovery and increase research costs via IRB review. For these reasons, synthetic data that preserve patient-variable relationships have been an active area of research. We discuss current advances made by generative models in this area and the breakthrough AI technologies accelerating those advances.

Peter Andersen, Advanced Advisory Consultant, Life Sciences Advisory, NNIT

Automation and digital transformation projects are often seen failing. This is commonly due to the lack of a proper data foundation. Join this presentation and learn how to enable data transformation through the three core data activities that you should have in place: NLP, advanced analytics and external standards.

10:55 am Session Break
11:10 am LIVE Q&A:

Session Wrap-Up Panel Discussion

Panel Moderator:
Kimberly Robasky, PhD, Head, Translational Science, Renaissance Computing Institute (RENCI)
Panelists:
Nick Lynch, PhD, Founder & CTO, Curlew Research
Peter Andersen, Advanced Advisory Consultant, Life Sciences Advisory, NNIT
11:30 am Lunch Break - View Our Virtual Exhibit Hall
11:35 am Interactive Breakout Discussions

Consider joining a breakout discussion group. These are informal, moderated discussions with brainstorming and interactive problem solving, allowing participants from diverse backgrounds to exchange ideas and experiences and develop future collaborations around a focused topic.

Timothy Gardner, CEO, Riffyn, Inc.

How do you use data / digitization today to drive scientific discovery / product development?

What are you greatest scientific pain points / gaps that are not being met by digitization?

What kinds of outcomes do you believe digital tools could help you achieve?

 

Scott Jeschonek, Principal Program Manager, Microsoft Azure

Welcome to this discussion group on the growth of demand for HPC in scientific research. We are looking forward to a lively forum. We'll start by looking at three related topics:

- What events trigger demand in your organization? How has the current pandemic impacted resources?

- What could make scale and collaboration more accessible to more researchers?

- Share a recent experience of shifting workloads to manage HPC capacity.

Greg DiFraia, General Manager, Americas, Executive Team, Scality
Shailesh Manjrekar, Head of AI and Strategic Alliances, Executive Team, WekaIO

In this session we’ll discuss how to provide researchers with performance and scale in genomics & research analytics, to drive results at a price point that’s economically viable on public & private cloud.

11:35 am

Breakout: NGS Pipeline Optimizations

Tristan J Lubinski, PhD, Sr Scientist, Next Generation Sequencing Informatics, AstraZeneca Pharmaceuticals; Co-organizer, Boston Computational Biology and Bioinformatics (BCBB)
Howard Marks, Technologist Extraordinary and Plenipotentiary, VAST Data

Storage solutions we’ve been using force bioinformaticists to make trade-offs between the capacity and low-cost of disk and the performance of flash. This results in complex tiering configurations that only deliver performance for a small slice of the data. In this session, we will review how advancements in technology enable VAST Data to revolutionize the cost of all-flash and allows bioinformatists faster analysis across larger datasets for deeper insights.

PLENARY KEYNOTE PROGRAM

12:00 pm

Welcome Remarks

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute
Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP
12:15 pm

Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert C. Green, Professor & Director, G2P Research, Genetics & Medicine, Brigham & Womens Hospital
12:40 pm

AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Z. Jovanovic, PhD, Chief Digital Officer, Sanofi
1:05 pm

LIVE Q&A: Session Wrap-Up Panel Discussion

Panel Moderator:
Vivien R. Bonazzi, PhD, Managing Director & Chief Biomedical Data Scientist, Deloitte Consulting LLP
Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP
1:25 pm Happy Hour - View Our Virtual Exhibit Hall
2:00 pm Close of Conference





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