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. 

We reduced the alignment time for a 640 million read human RNA-sequencing dataset from 19 hours to 1 minute using serverless cloud computing. We make the methodology accessible to researchers who are not cloud experts by providing a graphical interface for the accelerated workflow.  We illustrate how this 1100-fold computational speedup allows biomedical scientists to experiment with alignment parameters which impact the final analytical results.

Whole exome and whole genome sequencing provide hundreds of thousands of genetic variants per patient, however, of them only very few are pathogenic. Current computational methods are inefficient in differentiating pathogenic mutations from neutral genetic variants that are predicted to be damaging and cannot predict the functional outcome of mutations. We will present deep learning approaches and machine learning methods in the role of detecting pathogenic mutations. Visualization tools for better utilizing NGS data will be presented to understand human disease genomics.

Tumors use alternative promoters to increase isoform diversity, to activate oncogenes, and to evade host immune attacks. ChIP-seq has been the gold standard to measure promoter activities, but scarcity of fresh starting materials restricts interrogation of individual patient tumors. We present MethylToActivity (M2A), a deep-learning framework for inference of H3K4me3 and H3K27ac levels from DNA methylomes. M2A accurately reveals promoter activities in individual tumors, captures tumor-type specific promoter usages, and is generalizable to various pediatric and adult cancers.

In less than a decade, CRISPR has evolved from a bacterial immune system to the foundation of a powerful, flexible genome editing technology that has already transformed biomedical research, spawned a $10-billion biotech industry, and is poised to make major strides in the clinic. Kevin Davies, the founding editor of Bio-IT World, has spent the past few years working closely with the CRISPR community as the Executive Editor of The CRISPR Journal. In this talk, he shares highlights of his new book, EDITING HUMANITY, published this week, which explores the genesis of the CRISPR revolution, its impact on the gene therapy field, and the recent scandal involving the birth of CRISPR babies in China.

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.