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Webinar
This presentation describes how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert.
This webinar presents the Statistics Ontology, STATO which is a semantic framework to support the creation of standardized analysis reports to help with review of results in the form of data matrices.
This presentation reviewed the challenges in identifying, acquiring and utilizing research data in relation to an evolving data market.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The Center for Expanded Data Annotation and Retrieval (CEDAR) has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community.inar.
In this webinar Dr Henriette Harmse from EMBL-EBI presents how they are using their ontology services at EMBL-EBI to scale up the annotation of data and deliver added value through ontologies and semantics to their users.
This webinar explores the application of new tools and metrics to assess and promote the FAIR principles across ELIXIR Core Data Resources, highlighting the approach, findings, and lessons learned.
Toolkit
This third webinar in our Blockchain Education series explores the challenges and key considerations for implementing Blockchain technology in Healthcare, highlighting its potential to transform information exchange with greater security and trust.
In this webinar, we discuss how Elsevier is increasingly leveraging the FAIR Guiding Principles to improve its products and services to better serve the scientific community.
This webinar features UX specialists introducing User Experience and exploring how it can address design, trust, and ethical challenges in applying AI to life sciences.
