The research objective of MPrint-OKN is to create a valuable collaborative system that develops and distributes the most advanced molecular models, machine learning, data science, and data visualization tools available to the many disciplines requiring molecular systems for product development. We seek to reduce both the cost and time of discovering and developing next-generation molecular-based applications by placing advanced tools in the hands of more researchers. This presentation will cover our development of the portal and a discussion of some of the tools and strategies we are using to centralize molecular AI activities.

Utilizing a revolutionary combination of contextual language processing and memory generation with components of artificial intelligence, the platform enables rapid human interpretation and hypothesis generation (Augmented Intelligence). Direct assessment of disease processes, target identification, drug mechanism of action, safety concerns, and the identification of translational mechanisms between animal models and human disease can occur in hours or days, instead of the weeks to months traditionally required to reach this depth of understanding.

The Operational Design Center at EMD Serono has leveraged causal machine learning to analyze hundreds of variables across thousands of clinical trials with the objective of identifying causal drivers of enrollment success. Understandings gained from these efforts allow for creating a predictive algorithm for optimizing study design to conduct faster, less expensive trials. Early insights will be shared.

Many critical facts required by healthcare AI applications are locked in unstructured free-text data. Recent advances in deep learning have raised the bar on achievable accuracy for tasks, like named entity recognition, entity resolution, de-identification and others, using novel healthcare-specific networks and models. Dr. Sharma will discuss how Roche applies the greatest advances in AI for healthcare to extract clinical facts from pathology reports and radiology. Dr. Sharma will then detail the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale.

Over the past several years there has been a significant increase in the share of patient records stored electronically. Variations in the major EMR systems, the challenges of secure permissions and other factors have made secure exchange of sensitive information, particularly at scale, quite difficult. FHIR effectively addresses these issues, and facilitates data sharing to permit access from small cohorts to enormous datasets. This capability is foundational for the wider application of AI to healthcare issues, and will help provide much better access to shared healthcare datasets. Prospects for the future, ongoing challenges and promising opportunities will also be discussed.

AI technologies, such as natural language processing (NLP), are expanding their use of deep learning methods. We will review what NLP is, uses of Machine Learning (ML) within NLP, and use of NLP for wider ML projects in decision making. We will describe real-world use cases to improve patient outcomes.

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. 

AbbVie is implementing the AbbVie CHILE (Crystallography with Human-In-Loop Enhancement) platform where machine learning models use human-in-loop feedback to improve classifications at important decision points in the process of structure identification. RPA and other automation technologies provide integration with external and internal systems to handle repetitive and directive tasks. Our work illustrates how combining the power of multiple digital technologies can help transform scientific workflows and deliver operational, analytical, and experiential value to our scientists.

AstraZeneca is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. This is enabled though the use of an extensive range of cutting-edge imaging technologies that support studies into the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies, and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis, which integrate spatial relationships in unprecedented detail.

In 2019, major life sciences companies mobilized to form a pre-competitive, collaborative quantum computing working group (QuPharm) and delineate a framework and approach to accelerate realizing the potential of quantum acceleration in drug discovery. Learn from industry thought leaders on how to valuate and map problems into quantum algorithms, set up organizations to enable and scale quantum computing pilots and establish effective cross-industry, tech, and start-up collaborations.

AI is at peak hype at present, but its full potential will not be realized unless a clear and incremental strategy is adopted. In this talk, we will discuss the current state of collaboration and pre-competitive activities within AI in Life Sciences R&D, where this is heading, and how we can influence it as a community. We will discuss how data sharing and the use of data standards will need to underpin the future potential of AI and look ahead to the future approaches.

Rare disease patients suffer too often from long diagnostic delays and misidentified diseases. This creates a significant burden, not just for patients, but for healthcare systems. We present in this talk examples of instances where we have collaborated with researchers and hospital systems to develop novel approaches for rare disease patient identification using tools like genomics, machine learning, and NLP.

Researchers use biomarker and outcomes data to model and predict adverse events. However, access restrictions to safeguard patient privacy necessarily slow down the rate of discovery and increase research costs via IRB review. For these reasons, synthetic data that preserve patient-variable relationships have been an active area of research. We discuss current advances made by generative models in this area and the breakthrough AI technologies accelerating those advances.

Automation and digital transformation projects are often seen failing. This is commonly due to the lack of a proper data foundation. Join this presentation and learn how to enable data transformation through the three core data activities that you should have in place: NLP, advanced analytics and external standards.

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.