University of Cambridge > Talks.cam > CMS Seminars from business and industry > Math to Medicines: Accelerating Drug Discovery, Development and Repositioning using Clinical Trial Data Mining and Machine Intelligence

Math to Medicines: Accelerating Drug Discovery, Development and Repositioning using Clinical Trial Data Mining and Machine Intelligence

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The drug development process is a time-consuming and expensive endeavor. The application of data science and artificial intelligence methods could potentially improve drug discovery, development, and repositioning. Collectively, the application of big data tools and methods could help handle the high dimensionality of the data and help develop novel modelling strategies to link early experimental analysis to clinical outcomes. In this talk, I will discuss three key ideas: novel methods for clinical trial data mining (SAEgnal, TrialGraph, ClinicalTrials2Vec), multi-omics data integration (Omicsfold and BlockRank) and digital drug repositioning. Drug development teams could use the collective insights and models to augment trials using data-driven approaches, improve patient engagement, optimize side effects, uncover novel indications, and ultimately accelerate drug discovery.

This talk is part of the CMS Seminars from business and industry series.

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