Pharmaceutical companies are undergoing a digital transformation. By experimenting with new initiatives, they are positioned to play a role in the revolution of healthcare. This transformation is driven by data from internal and external sources, including both -omic data and that from digital devices. Bio-IT World WEST, part of Molecular Medicine Tri-Conference, brings together all the stakeholders involved in this transformation. Your registration includes the option to track-hop to the other programs at the Molecular Med TRI-CON as well as the Hackathon, plenary keynote, exhibit/poster hall, and more!

Data is being constantly produced, in many disparate forms, but can easily become silo’d and inaccessible. Join us at Software Tools, Services, and Applications, part of Bio-IT World West and the Molecular Medicine Tri-conference as we discuss best practices to store, analyze and share biomedical data within and outside of your company.

Final Agenda

Monday, March 2

10:30 Conference Program Registration Open

KEYNOTE SESSION

(please see Keynotes page for details)

11:45 Organizer’s Opening Remarks

Cindy Crowninshield, RDN, LDN, HHC, Executive Event Director, Cambridge Healthtech Institute

11:50 Chairperson’s Remarks

Allison ProffittAllison Proffitt, Editorial Director, Bio-IT World


11:55 Keynote Introduction, Benchling

Ashoka Rajendra, Head, Product, Registry, Inventory, Benchling


12:10 pm KEYNOTE PRESENTATION: The AI Bubble and the Emerging Thinking Economy

Michelucci PietroPietro Michelucci, PhD, Director, Human Computation Institute

This presentation presents a realistic assessment of the “AI bubble” – where there is value, where there is hype, and how human-in-the-loop computing gives us futuristic AI capabilities today that co-evolve with AI technology and even help improve AI.

12:40 PANEL DISCUSSION: Data Quality in Human Computation Systems

Is today’s artificial intelligence fervor based on hype or is it happening? We’ve seen some amazing results from AI-based systems, fueled by increases in processing speed that render traditional applications finally practicable. At the same time, powerful new techniques are emerging including fruitful human/AI partnerships and recent successes based on combining crowdsourcing with machine learning. These new methods dovetail nicely with special challenges posed by precision medicine, often entailing complex interdependencies among data acquisition, analysis, privacy, and ethics. That said, they also introduce a new set of challenges as we navigate issues of transparency, trust, and reliability where automated systems are involved. This panel will discuss recent work in online collective systems that combine human and machine-based information processing in the biomedical space, how these systems could be applied to precision medicine, and how to avoid some of the potential pitfalls associated with these approaches. We also discuss an information processing ecosystem designed to accelerate precision medicine research while mitigating associated complexity and resource needs.

Moderator:

Allison ProffittAllison Proffitt, Editorial Director, Bio-IT World


Jennifer CouchPanelists: Jennifer Couch, PhD, Chief, Structural Biology and Molecular Applications Branch, Division of Cancer Biology and Citizen Science Coordinator, National Cancer Institute


Krotman_DevinDevin Krotman, Director, IBM Watson AI XPRIZE


Mandavi_VaniVani Mandava, Director, Data Science Outreach, Microsoft Research


Michelucci_PietroPietro Michelucci, PhD, Director, Human Computation Institute


Tsueng_GingerGinger Tsueng, PhD, Scientific Outreach Project Manager, Department of Integrative, Structural and Computational Biology, The Scripps Research Institute


 

Amazon 1:30 Bio-IT World WEST Luncheon Presentation: Accelerating the Exchange of Data in Healthcare and Life Sciences

Fred Lee, MD, MPH, Head, Health Care, Life Sciences Business Development, AWS Data Exchange, AWS

Predictive models and algorithms in healthcare and life sciences (HCLS) have emerged from the combination of patient data and advanced analytics. With machine learning and AI technologies becoming commoditized, scalable access to patient data now throttles the build of such predictive analytics. We will discuss how the AWS Data Exchange, as a digital marketplace for data, addresses this ‘data bottleneck’ by accelerating data exchange in a regulatory compliant, economically sustainable, and cloud-native manner.

2:05 Session Break

PLATFORMS TO FACILITATE DISCUSSION

2:20 Chairperson’s Remarks

Matthew Trunnell, Vice President and Chief Data Officer Director, Hutch Data Commonwealth

2:25 Establishing a Regional Data Commons

trunnell_matthewMatthew Trunnell, Vice President and Chief Data Officer Director, Hutch Data Commonwealth

The focus of the commons will be enabling discovery of and access to life sciences research data and healthcare data to advance research and innovation. That is, the principal initial stakeholders are life & health sciences researchers and technology organizations looking to innovate in this space. We currently have three workstreams: one around data discovery; one around privacy-preserving technologies (differential privacy, synthetic data, etc.) to facilitate access to clinical data; and a third around governance focused on streamlining the process of establishing data use agreements.

2:55 Turning WGS Genetic Testing into a Dialogue between Physicians and Labs with GenomeDiver

Stolte_ChristianChristian Stolte, Consultant, Icahn School of Medicine, Mt. Sinai

Developed as part of the NYCKidSeq project, GenomeDiver fosters a dialogue between the clinician and genetic testing lab. The software leverages the physician’s knowledge of their patient by asking them to provide additional information to the lab, which then forms the basis for reanalysis. It delivers understandable information about mutations in the entire genome, using knowledge about functional variants coming from an increasing number of public sources, in particular the GTEx project.

3:25 NEW: Using Modern Frameworks to Process Genomic Data at Scale

Mikkilineni_RajeshRajesh Mikkilineni, Lead Data Engineer, Data Engineering & Artificial Intelligence, Takeda

Using a generic framework like Hail and a scalable data procession framework like Apache Spark to processing big genetic data set to power scientific analysis. These frameworks enable us to perform quality control at sample and variant level, apply VEP annotation, and run PheWas and other statistical analyses on genetics data at scale.
Presentation delivered via a live, interactive video conferencing platform.

3:55 Refreshment Break and Transition to Plenary Keynote


PLENARY KEYNOTE SESSION

(please see Keynotes page for details)

4:35 Welcome Remarks

Cindy Crowninshield, RDN, LDN, HHC, Executive Event Director, Cambridge Healthtech Institute

4:45 PLENARY KEYNOTE INTRODUCTION

Thomas Westerling-Bui, PhD, Senior Scientist, Regional Business Development, Aiforia

5:00 PLENARY KEYNOTE PRESENTATION: High-Performance Medicine

Eric Topol, MD, Founder and Director, Scripps Research Translational Institute (SRTI); Author, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

 

 

 

 

6:00 Grand Opening Reception in the Exhibit Hall with Poster Viewing, Speed Networking, Book Signing, and Meetup Group

7:30 End of Day

Tuesday, March 3

7:30 am Registration Open and Morning Coffee

KEYNOTE SESSION

(please see Keynotes page for details)

8:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, HHC, Executive Event Director, Cambridge Healthtech Institute

8:05 NEW: Chairperson’s Remarks

Annastasiah Mudiwa Mhaka, PhD, Founder and Principal, Mawambo Lifesciences; Co-Founder, Convenor and Former President, Alliance for AI in Healthcare (AAIH)

8:10 Keynote Sponsor Introduction

Rangadass_VasuVasu Rangadass, President, CEO, L7 Informatics, Inc.

 

 

 

 

 

8:25 KEYNOTE PRESENTATION: AI and Big Data Strategies in Accelerating Clinical Research for Faster Rare Disease Cures

Rajasimhja HarshaHarsha Rajasimha, MS, PhD, Founder, Jeeva Informatics Solutions, Inc.; Founder and Chairman, IndoUSrare; Co-Director, Rare Diseases Systems Biology Initiative, George Mason University

After losing a child to a rare congenital disease, Dr. Rajasimha became determined to apply his clinical genomics data research experience to develop solutions to help accelerate clinical research leading to faster cures for rare disease. Dr. Rajasimha will discuss his efforts in fostering collaborative bridges between patient advocacy groups and researchers in the USA and their counterparts in India to help accelerate clinical research, trials, and therapy access across borders. The talk will include recent global initiatives to accelerate screening, diagnosis, and treatments of rare and undiagnosed diseases. He will also share work on the development of an AI-driven digital health platform to improve clinical trial operational efficiencies while significantly reducing costs and travel burden on patients.

8:55 PANEL DISCUSSION: Applications of AI Technologies in Pharmaceuticals: Facilitating Development of Therapeutics in Treating Rare Diseases

The complex research framework involving industry, academia, and government to discover and develop new therapeutic products makes drug discovery a laborious process. With rapid strides that life sciences companies are making in the fields of gene and cell therapies, -omics technologies, and smart molecule approaches, an urgent need exists for cost-effective, time-effective, and advanced technologies to analyze large databases of information to help develop novel therapies. Organizations are recognizing the value of AI-based platforms and tools to leverage data to find hidden drug-disease correlations. Also, structured and unstructured data can be derived from multiple sources as never before. This panel brings senior level experts in pharma, AI-based technology, and government to discuss the role of AI platforms and tools to establish a robust pipeline as part of drug discovery portfolio and address new therapeutic areas, including rare diseases.

Mhaka_AnnastasiahNEW: Moderator: Annastasiah Mudiwa Mhaka, PhD, Founder and Principal, Mawambo Lifesciences; Co-Founder, Convenor and Former President, Alliance for AI in Healthcare (AAIH)


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


Rajasimhja_HarshaHarsha Rajasimha, MS, PhD, Founder, Jeeva Informatics Solutions, Inc.; Founder and Chairman, IndoUSrare; Co-Director, Rare Diseases Systems Biology Initiative, George Mason University


Rangadass_VasuVasu Rangadass, President, CEO, L7 Informatics, Inc.


 

9:40 Refreshment Break in the Exhibit Hall with Poster Viewing, Speed Networking, Book Signing, and Meetup Group

ESTABLISHING A KNOWLEDGE NETWORK FOR RARE DISEASES

10:40 Chairperson’s Remarks

Ryan Leung, Vice President, Strategy & Corporate Development, Research to the People

10:45 Rare Diseases: Starting at the Beginning

Liebman_MichaelMichael Liebman, PhD, Managing Director, IPQ Analytics, LLC

Rare diseases, typically pediatric, are notoriously heterogeneous; they are difficult to diagnose and manage except in limited cases. We are establishing a “knowledge network” involving clinicians and researchers, patients and families, and implementing it within a platform that looks at the fetus and the effects of maternal lifestyle, environment and clinical history on the evolving stages of organ system development to evaluate where and how risk may develop for (rare) diseases. This involves an international collaboration and is targeting the identification of biomarkers and behaviors that indicate risk and may enable early detection and even prevention/mitigation. The platform initially examines lung development and disease risk, e.g., ARD and BPD, and will enable integration of existing studies and extension to other organ systems.

11:15 Accelerating Research in Rare Disease through Patient-Partnered Collaborations

Leung_RyaRyan Leung, Vice President, Strategy & Corporate Development, Research to the People

Patient-centricity is becoming increasingly important in all areas of healthcare, but this is particularly the case for rare diseases. With so few patients, it is critical that we make the most out of every patients' story and experience, engaging them at every point of research, development, care, and treatment. At Research to the People, we partner with patients directly to help them access and understand their health data. Leveraging advances in -omics, bioinformatics, deep learning and cloud computing, alongside a powerfully diverse community of physicians, scientists and patient advocates, we've created a uniquely collaborative platform for open-source rare disease research. With 5 successful collaborations to date, we're incredibly excited by the future of patient-partnered healthcare.

11:45 Strategies to Study Rare Diseases with “Big Data” 

Jaclyn N. Taroni, PhD, Principal Data Scientist, Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation 

We sometimes speak of “big data” in biology. In most cases, these data are wide, and have many more features than examples. This is particularly pronounced in the case of rare diseases, where we may have tens of samples but tens of thousands of measurements. I’ll discuss how we can use compendia of data with many training examples as a training dataset and then transfer the results of those analyses to rare disease datasets where the number of samples is particularly limited. I’ll also discuss how this feature of data, even outside of rare diseases, affects deep learning methods in this domain.  

12:15 pm Session Break

CAS_New 12:20 BIO-IT WORLD WEST CO-LUNCHEON PRESENTATION I: Describing Chemistry to Algorithms: Why Scientific Expertise Improves Accuracy

Lee_AlphaAlpha Lee, PhD, Doctor, Physics, University of Cambridge


McBride_MatthewMatthew McBride, MS, Director, Science IP, CAS

If a picture is worth a thousand words, then a chemical structure is worth thousands of features.  Join Dr. Alpha Lee from the University of Cambridge to see how impactful descriptors are on predictions.  If your AI initiatives aren’t meeting expectations, see how better representations of chemistry structures improve algorithm performance.  CAS descriptors are derived from centuries of scientific knowledge and are proven to improve AI accuracy.

Zifo 12:50 BIO-IT WORLD WEST LUNCHEON PRESENTATION II: Towards the Digital Lab

Paul Denny Gouldson, Chief Digital Officer, Digital Solutions, Zifo RnD Solutions

1:20 Refreshment Break in the Exhibit Hall with Poster Viewing, Speed Networking, Book Signing, and Meetup Group

2:00 Breakout Discussions in the Exhibit Hall (please click here for details)

3:00 Transition to Keynote Session

KEYNOTE SESSION

(please see Keynotes page for details)

3:15 Organizer’s Remarks

Christina Lingham, Executive Director, Conferences and Fellow, Cambridge Healthtech Institute

3:20 Keynote Introduction

Allison Mallory, PhD, Director, R&D Molecular Biology, Stilla Technologies

3:35 What Does the New Era of Genomic Medicine Look Like? Effects on Patient Care, Therapeutics, and Diagnostics

20 years after the completion of the first draft of the Human Genome Project, there is compelling evidence of genomics delivering the rich promise of precision medicine. There have been major advances in the throughput and affordability of genome sequencing, enhanced tools for genome analysis and interpretation, new paradigms for therapeutics and strong signs of clinical benefit using genome editing. But major challenges remain. In this special plenary roundtable, three established pioneers of genomic medicine – David Haussler, Stephen Kingsmore, and Liz Worthey – offer their insights on the extraordinary advances in genomic medicine over the past 1-2 decades and share their hopes and concerns for the future of our field.

Davies_KevinModerator: Kevin Davies, PhD, Executive Editor, The CRISPR Journal, Mary Ann Liebert, Inc.


Kingsmore_StephenPanelists: Stephen Kingsmore, MD, DSc, President/CEO, Rady Children’s Institute for Genomic Medicine


Haussler_DavidDavid Haussler, PhD, Investigator, Howard Hughes Medical Institute; Distinguished Professor, Biomolecular Engineering, University of California, Santa Cruz; Scientific Director, UC Santa Cruz Genomics Institute; Scientific Co-Director, California Institute for Quantitative Biosciences (QB3)


Worthey_LizElizabeth Worthey, PhD, Director, Genomic Medicine, University of Alabama, Birmingham School of Medicine


4:50 Spring Fling Celebration in the Exhibit Hall with Poster Viewing, Speed Networking, Book Signing, and Meetup Group

6:00 End of Day

Wednesday, March 4

6:45 am Registration Open

7:00 BREAKFAST PANEL DISCUSSION: The Time is NOW: Creating Meaningful Change for Women in the Workplace (Sponsorship Opportunity Available)

(please see Women in Science page for details)

Toft_RobinModerator: Robin Toft, Author of WE CAN, The Executive Woman’s Guide to Career Advancement; Founder and Chairman, Toft Group Executive Search


Samuels_CamillePanelists: Camille Samuels, MBA, Partner, Venrock


Hastings_PaulPaul Hastings, President and CEO, Nkarta Therapeutics, Inc


Wright_TerryTeresa L. Wright, MD, Staff Physician, Medicine, San Francisco Veterans Administration


KEYNOTE SESSION

(please see Keynotes page for details)

8:00 Organizer’s Remarks

Mana Chandhok, Conference Producer, Cambridge Healthtech Institute

8:05 Chairperson’s Remarks

Joseph Ferrara, CEO, Boston Healthcare

8:10 Keynote Introduction

Fred Lee, MD, MPH, Head, Health Care, Life Sciences Business Development, AWS Data Exchange, AWS

 

8:25 KEYNOTE PRESENTATION: The Value and Application of Informatics in Cancer Care Delivery

Patt_DebraDebra A. Patt, MD, Vice President, Public Policy & Academic Affairs, Medical

Oncologist, Texas Oncology Cancer Center & Editor in Chief, Journal of Clinical Oncology-Clinical Cancer Informatics


8:55 KEYNOTE PANEL DISCUSSION: Pragmatic Use of Informatics in Cancer Care Delivery and Cancer Research: Big Data and AI Take on Cancer

Moderator: Joseph Ferrara, CEO, Boston Healthcare

Panelists: Mark Hulse, Chief Digital Officer, City of Hope

Debra A. Patt, MD, Vice President, Public Policy & Academic Affairs, Medical Oncologist, Texas Oncology Cancer Center & Editor in Chief, Journal of Clinical Oncology-Clinical Cancer Informatics

Kristin Beaumont, PhD, Assistant Professor, Assistant Director of Single Cell Genomics Technology Development Icahn Institute, Dept. of Genetics & Genomic Sciences, Icahn School

Ajay Shah, PhD, Executive Director & Head of IT for Translational Medicine, Bristol-Myers Squibb

Paul A. Rejto, PhD, Vice President, Head of Translational Research, Pfizer Oncology R&D

 

9:40 Refreshment Break in the Exhibit Hall with Poster Viewing, Speed Networking, Book Signing, and Meetup Group

10:40 Chairperson’s Remarks

Shanrong Zhao, PhD, Director, Computational Biology, Pfizer

CLINICAL CARE DECISION SUPPORT

10:45 From Development to Deployment: Lessons Learned from Application of Machine Learning in Oncology Decision Support

Zahra ‘Nasim’ Eftekhari, Senior Manager, Head of Applied AI and Data Science, City of Hope

11:15 Leverage Sage Data Lake for Translational Medicine Biomarker Analytics

Ying (Sherry) Li, PhD, Lead IT Business Partner – Precision Medicine, Translational Medicine IT, Bristol-Myers Squibb

Clinical biomarkers have shown great promise to improve drug development efficiency and to understand target engagement, drug efficacy, as well as clinical endpoint prediction. At Bristol-Myers Squibb (BMS), biomarker research is a routine practice for our ongoing clinical trials. To make data findable, accessible, interpretable and reusable (FAIR), we process and manage hundreds of BMS clinical trials’ biomarker data into our Sage Data Lake and integrate that with clinical information from Oracle Clinical and Rave databases. This information is fed into the SignalsTranslational (Signals) application (co-developed by BMS and Perkin Elmer) as well as Sage Clinical database. Using Sage Signals, our scientists can track biomarker assays, analyze biomarker data cross studies/diseases, drill into platform specific concerns, which help clinical programs to make informed decisions. We will share some use cases in our presentation and discuss how Sage Data Lake helps our biomarker research.

CUTTING-EDGE ALGORITHMS FOR SEQUENCING

11:45 PANEL DISCUSSION: Cutting-Edge Algorithms for scRNAseq

Zhao_ShanrongModerator: Shanrong Zhao, PhD, Director, Computational Biology, Pfizer


Panelists: Rob Patro, PhD, Assistant Professor, Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland

Jeffrey Rosenfeld, PhD, Manager, Biomedical Informatics Shared Resource, Assistant Professor of Pathology and Laboratory Medicine, Rutgers Cancer Institute of New Jersey

 

12:45 Enjoy Lunch on Your Own

1:20 Refreshment Break in the Exhibit Hall with Last Chance Poster Viewing, Speed Networking, Book Signing, and Meetup Group

FEATURED SESSION: DATA STRATEGIES FOR GENOMICS

2:00 Chairperson’s Remarks

Geraldine A. Van der Auwera, PhD, Director of Outreach and Communications, Data Sciences Platform, Broad Institute

2:05 Fake It ‘til You Make It (Reproducible): Synthetic Data Resources for Genomics

VanderAuwera_GeraldineGeraldine A. Van der Auwera, PhD, Director of Outreach and Communications, Data Sciences Platform, Broad Institute

The computational reproducibility of published biomedical research is limited by data access restrictions, affecting not just researchers who wish to reuse published analysis code, but also tool developers and educators who lack suitable example data for testing and training. We present: 1) a prototype pipeline that wraps established open-source data simulation tools to generate publicly shareable synthetic sequence data at any scale; and 2) a plan to develop community resources.

2:35 Drug Targets with Genomic Support: A Genomics-based Strategic Framework for Improving Target Discovery and Accelerating Drug Development

Justin Wade Davis, PhD, ACOS Research Fellow, Director, Computational Genomics, Genomics Research Center (GRC), AbbVie

Despite strong vetting for disease activity, only 10% of candidate new molecular entities in Ph1 clinical trials are approved. Analyzing historical data, Nelson et al. 2015 concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. We extend this using updated data, test prospectively whether genetic evidence predicts future successes, and introduce statistical models adjusting for target and indication-level properties.

 

3:05 Close of Conference