Biomedical research over the last decade has become increasingly complex. The field recognizes the need to bring different disciplinary experts together to solve challenging scientific questions. No longer is a single disciplinary perspective enough for truly breakthrough research advances. It is the science and the possibility of making a major advance that brings people together to form a research team. Once the team has been assembled, it becomes critical for the leader(s) of the team to recognize that there is much more than the science to tend to.

Biomedical researchers have access to many data sources, but finding data with specific characteristics remains a challenge. Datasets have different metadata, format, and structure. At the Broad Institute, we envision a simpler and more comprehensive search capability to allow researchers to find and reuse data across many datasets. We propose a cross-domain data model built specifically to facilitate search and reuse. We share our methods, lessons learned, and status.

Our multi-lab collaborative partnership will pilot an integrated, community-centric framework within 27 months to fully leverage existing microbiome data science resources and high-performance computing systems available within the DOE complex for data access, integration, and advanced analyses. In this talk I will cover some of the challenges in microbiome data sciences and how we aim to overcome these by creating a large, open-access repository of FAIR data.

Sage is a comprehensive platform that enables FAIR data, for data ranging from discovery, clinical research, and real-world. This talk will focus on the overview of Sage and solutions developed in Sage ecosystem for biomarker analytics, including an overview of essential components of the platform, such as uniform high-quality data ingestion, data lake enhancement with semantic integration conformance of data, and a reproducible research framework.

Sage ecosystem is a cross-functional cohesive platform for finding, accessing, integrating, and analyzing patient-centric data. This talk will focus on real-world data (RWD). It will highlight how Sage catalogs, models, integrates, conforms, and presents patient-level metadata across all RWD assets to facilitate downstream cross-dataset analysis within an integrated managed analytics environment. This talk will touch on the business drivers for this initiative, our current progress, as well as some lessons learned.

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

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

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.

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. 

There is growing appreciation in the scientific community for the value of making data from exploratory pilot projects and published studies available for reuse. With the aim of accelerating new discoveries through the sharing and collaborative re-analysis of data, the University of Chicago has created open source software called “Gen3” for building data commons, which are cloud-based platforms for harmonizing, sharing, and analyzing large datasets contributed by multiple groups or organizations. Background and overview of the Gen3 software architecture and how it is being used will be presented.

Since launching in October 2018, the Gabriella Miller Kids First (Kids First) Data Resource Center (DRC), has made an increasing number of pediatric genomic studies available to the research community. A “best-of-breed” approach has been taken by our multi-institutional team to develop a platform comprising of reusable technology stack components enabling search and query capabilities coupled with secure workspaces for data analysis. Additionally, the DRC services strive towards a foundation for interoperability with other large-scale data sources, both nationally and globally.

BMS asked BioTeam to build a Data Commons to share FAIR scientific data across their organization. Through work at NCI, BioTeam became acquainted with the open-source Gen3 Data Commons framework developed by the University of Chicago and we chose to leverage Gen3 for this BMS Cloud platform. In this talk, we will describe our experience as the first organization outside of U. Chicago to build a Gen3 Data Commons.

Sharing genomics datasets for collaborative analyses poses critical challenges for pharmaceutical organizations with diverse R&D needs. In 2019, IT for Translational Medicine created “SiloBreaker”; a cross-functional program with the mission to break down the barriers that exist between genomics data and scientists. The team implemented the Gen3 Data Commons framework as its key platform solution. This talk will discuss the significant improvement in the culture of genomics data sharing at BMS achieved through the SiloBreaker program.

The OHDSI (Observational Health Data Sciences and Informatics) COVID-19 studyathon in March 2020 produced a number of high profile studies, such as the study of the safety profile of hydroxychloroquine in real world data from nearly 1 million prevalent users of the drug across the globe, which went on to inform public health guidance from a.o. the European Medicines Agency (EMA). The volume and speed of the studies is interesting, since these studies typically take many months to prepare and execute. In this talk, we will illustrate how the OHDSI community leverages the OMOP common data model, standardized analytics as well as a powerful global open science network to make this a reality without compromising statistical rigor or scientific standards. We will also cover the role of the IMI EHDEN project which is building a European network of health data sources to strengthen the OHDSI community and further observational research in real world health data.

This talk will explore the National Cancer Institute’s engagement in the Global Alliance for Genomics and Health (GA4GH) as a way of extending the ability of cancer researchers and physicians beyond the Cancer Research Data Commons to access a broader range of data and tools, potentially increasing the power of analyses of rare cancers.

Here we describe a radically open approach to diversify the AD drug portfolio. Using multi-omic and genetic models of disease built from human brain data, a suite of emerging therapeutic hypotheses are generated that complement the small set already in drug development. To catalyze rapid evaluation of these targets, target enabling packages – containing computational and experimental resources including prototype drug compounds – are developed and openly distributed for use across the research community.

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?

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.

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.

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.