Translational Learning and Informatics: Big data and AI to drive personalized precision medicines for complex human diseases (Prof. Nathalie Pochet)

When
24-06-2021 from 11:00 to 12:00
Where
Virtual event
Language
Dutch
Organizer
Tijl De Bie
Contact
tijl.debie@ugent.be
Website
https://www.meetup.com/UgentDSS/events/278405154
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Data Science seminar by Prof. Nathalie Pochet (Broad and Harvard Medical School)

Abstract

I will talk about the engineering and AI-driven algorithms and software tools that I (co-)developed, including M@CBETH, SERV, MetageneProjection, GenomeSpace, Trinity, Trinity-CTAT, TFUtils, *AMARETTO and AMARETTO-Hub, all critical tools that have significantly helped advance and overcome challenges in biomedical research for better understanding, diagnosis and treatment of human disease. For example, my tool SERV led to my fine mapping of the highly-elusive causal genetic variant for the rare dominant Mendelian kidney disease ‘medullary cystic kidney disease type 1’ (MCKD1), which was then extensively confirmed experimentally using an assay that allows for screening and alleviates challenges of living-relative kidney donation. Trinity has led to the initial characterization of the SARS-CoV-2 viral sequence and its abundance estimation. Recently, my *AMARETTO and AMARETTO-Hub tools led to my predicting drug treatments for chemoprevention of hepatocellular carcinoma of viral and other etiologies, including hepatitis C and B virus-induced hepatocellular carcinoma, and experimental validation confirmed that two novel predicted compounds indeed attenuate liver fibrosis and cancer development in rats, and thus, potentially offer a safe and low-cost approach for chemoprevention of hepatitis C and B virus-infected patients who are at greater risk of progressing to hepatocellular carcinoma. These and other examples emphasize the power of my computational approaches. I will explain how I solved these problems by using a variety of "modern" AI techniques, including concepts from transfer learning, (un)supervised machine learning, interpretable learning, multimodal and multiscale data fusion, network biology and medicine, knowledge graphs, and active learning.

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