Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real-world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state-of-the-art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.

In this talk, I will present three different pictures of clinical research landscapes–one depicting a single drug development trajectory; one depicting a family of development trajectories from drugs in the same class; and one depicting the entire research portfolio from ten pharmaceutical companies. I will argue that each of these pictures has something valuable to teach about how to make the research enterprise more efficient, transparent, and ethical.

To meet the growing demands from scientists to effectively extract deep insights from single cell RNA-seq datasets, we developed cellxgene VIP, a frontend interactive visualization plugin to cellxgene framework, which directly interacts with in-memory data to generate a comprehensive set of plots in high resolution, perform advanced analysis, and make data downloadable for further analysis. It makes large scale scRNA-seq data visualization and analysis more accessible and reproducible with the potential to become an ecosystem for the scientific community to contribute even more modules to the Swiss knife of scRNA-seq data exploration tool. The source code is available at https://github.com/interactivereport/cellxgene_VIP. 

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.

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. 

The ISO IDMP is a set of standards developed by regulators and industry to structure medicinal product information in a consistent manner. The GSRS is freely distributed software, developed in collaboration with NIH/NCATS, that implements the substance standard. The GSRS defines substances in medicinal products and related substances, such as targets, metabolites, and impurities, and links these substances to products, clinical trials, applications, and adverse events.

While developing G-SRS, the NIH/NCATS team developed useful standalone tools to help streamline registration, curation, and regulation of medicinal ingredients. This presentation will cover three such projects: an image to chemical structure recognition program (molvec); an abstraction layer allowing cheminformatics software to switch between underlying informatics frameworks at runtime (molwitch); and Inxight:Drugs, which incorporates and unifies marketing, regulatory status, rigorous drug ingredient definitions, biological activity, and clinical use.

Exploratory visualizations generated from clinical trials and real-world data sources provide important insights into safety, efficacy, and biomarker responses to novel and standard-of-care treatments. Automation of data updates in near-real time increases the impact of this information on decision-making.

Truly personalized healthcare is possible only if we have access to meaningful, diverse and integrated data at scale. We are working on an E2E Engine that will allow us to FAIRify our clinical and molecular data from planning, through ingestion to re-use. We are creating a solution to pool clinical data with molecular, genomics and digital biomarkers for diverse therapeutic areas to fully capitalize on Roche scientific data. To address the diversity of the scientific data we leverage a Data Commons approach to process data from source systems in order to pool and share them with scientists.

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.

This panel is useful for startups and young companies to learn what VCs are looking for to invest in new platforms and technology. Topics to be addressed include: 1) Explore trends and lessons learned with regards to how AI and digital health platform developers are strategically partnering with BioPharma to accelerate business and asset value; 2) How to collaborate with pharmaceutical and technology companies when you are a startup; 3) What are VCs looking for?; 4) Discuss how software and platform developers are creating value around tools to secure capital, including key challenges and opportunities, and 5) Examine tangible case studies of what is working and what isn’t within this dynamic of stakeholders.

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.