The future of medicine will be enabled by our understanding of genetic disease drivers. As the architects of the world’s largest database of genomic data paired with de-identified health records, the Regeneron Genetics Center (RGC) is aiming to improve human health through data innovation and collaborative knowledge sharing. Dr. Reid discusses initiatives like Project Glow and the RGC-UK Biobank exome consortium, which enable the global scientific community to tap key datasets and identify new, better ways of preventing and treating human disease.

This presentation will address the problem of biological complexity in which many factors, each of small effect size, collectively influence disease risk, development, complexity, and response to therapy in cancer and other complex diseases. By using innovative computational methods built around network representations of biological interactions, we can gain insight into the disease process, develop predictive biomarkers, and identify possible avenues of therapeutic intervention.

This presentation will illustrate the current methods that are used for determining the precise treatment of cancer rather than the standard chemotherapy methods.

mTOR system is a database I have designed for exploring biomarkers and systems-level data related to mTOR pathway in cancer. This database consists of different layers of molecular markers and quantitative parameters assigned to them through a current mathematical model. This database is an example of merging systems-level data with mathematical models for precision oncology.

Infectious disease affects several million individuals all over the world, particularly from developing countries. We have built a bioinformatics pipeline, which combines reverse vaccinology tools, network biology system, and text mining algorithms to analyze proteomes of pathogens, and ranks proteins based upon their propensity to be an optimal vaccine candidate. Our system compares various machine learning approaches, such as support vector machines, neural networks, ensemble learning, and decision trees.

Learn how TruSight™ Software Suite offers ready-made infrastructure to analyze and interpret rare disease variants.  Powered by DRAGEN variant-calling, this software platform can evaluate all rare disease variant types within a single interface. Intuitive variant filtering, visualization and curation enable laboratories to perform streamlined interpretation and generate customizable reports.

Multiple somatic variant callers have been created to identify variants that tumor cells acquire during their evolution. It is still unclear which variant caller, or combination of callers, is best suited to analyze tumor sequencing data.  Dell Technologies and NVIDIA highlight tools/technologies to maximize opportunities for new methods development.

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. 

As pharma is embracing digital and agile, new challenges for the retention and sharing of information are emerging. While the information standards of a validated environment remain, ad hoc processes and distributed cloud solutions are creating new islands of knowledge. In this presentation, I will present a knowledge strategy based on automated discovery and integration for a development department of several thousand scientists at Roche.

Making true “molecule”-“mechanism”-“observation” relationship connections is a time-consuming, iterative and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections. The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting resources from already constrained data science departments to refine and redo highly similar ad hoc searches. The result of this is impairment of both the pace and quality of scientific reviews. In this presentation, I show how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision-making. I will take the audience through our pilot journey and highlight practical learnings that should inform subsequent endeavors.

We conducted   in silico   screens trying to repurpose >100 compounds for ~4000 rare disease indications. Various data types were utilized (protein-protein interaction network, pathways, disease-driven genes, competitive intelligence, etc.), and different computational methods were implemented and evaluated. Some biologically interesting drug/disease pairs were observed.

Single-cell RNA-Seq has become an invaluable tool for studying biological systems in health and diseases. We introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. scPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories.

Cellxgene Gateway is an open source tool (https://github.com/Novartis/cellxgene-gateway) which allows you to use the Cellxgene Server provided by the Chan Zuckerberg with multiple datasets. I will introduce this tool in the context of a typical single-cell RNA-Seq analysis workflow, and touch on deployment issues in an enterprise cloud with a budget.

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.