Over the last years, the commercially available chemical space (with pharmaceutical relevance) has rapidly increased. Several providers today are offering catalogs consisting of several hundred millions of screening compounds. We built a new compound platform to enable browsing, searching, selection, and ordering of compound sets from these libraries. The platform offers these capabilities by standardizing and preprocessing all molecules, calculating relevant properties, and enabling access to these libraries by combining fast structure-based search with property and metadata filters. This presentation will present the overall architecture and highlight some of the challenges encountered during the implementation.

SeedMeLab, a web-based data management system, comprises a modular set of building blocks that could be configured and customized in an extensible manner. It enables to host data on the web/intranet with access controls and pluggable identity management. The expressive and extensible file system allows data, its description, and its discussion to be collocated, which catalyzes discovery. It enables rich presentation and visualization that aids in making data more insightful. The built-in web services, coupled with extension API, make it a powerful platform to realize FAIR data compliance. Research teams or science applications could spin up SeedMeLab with their own branding and domain with customized file-system and presentation layout.

As we embrace the multi-omics and single-cell era, organisations are striving to integrate and evolve existing systems to harness richer and bigger data/metadata. Drawing upon our experience with top pharma, agriscience and FMCG companies, we share lessons learned and technologies for transitioning from fragmented systems towards a FAIR data ecosystem.

Medical imaging is an important component of Life Science R&D, however, the incorporation of these data types and processing workflows into a digital transformation ecosystem is not trivial. At Flywheel we have developed a cloud-scale platform that allows for the capture, curation, processing and secure collaboration for large volumes of medical imaging data, associated metadata, and complimentary data types. Drawing upon our experience with top pharma, we share lessons learned when streamlining the ingestion of millions of data sets from siloed servers and external partners such as CROs and academic institutions.  Additionally, we illustrate automation, real-time data search & access and machine learning workflows.

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. 

Most data breaches represent a systemic breakdown along multiple lines of both technical and human factors. While many factors can contribute to an unauthorized release, the effort necessary to protect against these factors is not equal. This discussion will be from a holistic viewpoint of many security breaches, the breakdowns in fundamental security concepts which lead to the breaches, and the factors of paramount consideration in protecting an enterprise.

Governance provides a playbook for a biopharma company to manage security and privacy compliance. Good governance leads to a better managed goal and a focused IT environment. CyberHygiene today is critical for any company developing a drug or researching cures and trying to protect intellectual property, as well as subjects’ personal information. Regulations under FDA and FTC, as well as EU GDPR, can be complicated.

To facilitate privacy-preserving, ownership-based data governance, we developed two novel algorithms which can be used to implement flexible fine-grained protection of genomic data: a) dynamic privacy-preserving encryption of user-specified genomic regions; and b) ownership and utility-preserving watermarking of the sequencing data. This empowers individuals to control when, for how long, and for what purpose any portion of their genomic data is shared, all in an auditable manner.

File data is growing at 30% annually. From risk of data loss, to the certainty that data will grow faster than IT budgets, you can’t afford to be left behind. Transform your data management strategy with Igneous and stop risking your organization’s most valuable assets.

The focus on preventative care and providing the “right” services & prescriptions is heavily being scrutinized and monitored. In response, the health industry is making data-driven decisions to become more strategic in their care—increasing cost-efficiency while improving outcomes. Sound Data Integrity testing provides a powerful way to eliminate data issues.

Biotechnology companies are facing new challenges in the amount of data that needs processing for genomics analysis. What used to be Terabytes of data is now petabytes of data and beyond. This data needs to be collected, analyzed, processed and then ultimately retained for compliance and research purposes - resulting in massive data storage and management challenges, unsolvable by legacy technology solutions. Our session will explain how to leverage new all-flash storage and hybrid-cloud solutions to make genomics analysis run quantum leaps faster than before.

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.