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Webinar
User research is grounded on a collaborative relationship with our end users - scientists, patients, healthcare providers, or other internal colleagues. But what about when you can’t physically be with research participants – be it because of cost, time, or global pandemic?
This webinar sponsored by the Pistoia Alliance and the Quantum Economic Development Consortium (QED-C) focuses on the emerging opportunities at the convergence of Quantum Computing (QC) and pharmaceutical R&D.
This webinar, supported by Thermo Fisher Scientific, outlines progress on the Pistoia Alliance Lab of the Future Projects and including updates on the Methods Database and the Universal Integration Layer.
This webinar will demonstrate how implementing FAIR principles can accelerate R&D workflows by making data more actionable, reusable, and shareable, ultimately enabling faster science and enhanced collaboration.
It seems that AI is also becoming a buzzword, like design thinking. Everyone is talking about AI or wants to have AI, and sees all the ideas and benefits - that’s fine, but how do you get started?
In this webinar, NVIDIA will present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare.
Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.
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.
