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PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260922T101420Z
UID:Seminar-MIF-1396@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Othon Michail:MAILTO:Othon.Michail@liverpool.ac.uk
DTSTART:20221209T140000
DTEND:20221209T150000
SUMMARY:MIF Series
DESCRIPTION:Guo-Wei Wei: Mathematics and AI are revolutionizing Biosciences.\n\nMathematics underpins fundamental theories in physics such as quantum mechanics, general relativity, and quantum field theory. Nonetheless, its success in modern biology, namely cellular biology, molecular biology, biochemistry, genomics, and genetics, has been quite limited. Artificial intelligence (AI) has fundamentally changed the landscape of science, technology, industry, and social media in the past few years and holds a great promise for discovering the rules of life. However, AI-based biological discovery encounters challenges arising from the structural complexity of macromolecules, the high dimensionality of biological variability, the multiscale entanglement of molecular, cell, tissue, organ, and organism networks, the nonlinearity of genotype, phenotype, and environment coupling, and the excessiveness of genomic, transcriptomic, proteomic, and metabolomic data. We tackle these challenges mathematically. Our work focuses on reducing the complexity, dimensionality, entanglement, and nonlinearity of biological data. We have introduced persistent cohomology and various topological Laplacians, including evolutionary Hodge Laplacian, persistent Laplacian, persistent sheaf Laplacian, and persistent path Laplacian to model complex, heterogeneous, multiscale biological systems and thus significantly enhance AI's ability to handle biological data. Using our mathematical AI approaches, my team has been the top winner in D3R Grand Challenges, a worldwide annual competition series in computer-aided drug design and discovery for years. By further integrating with millions of genomes isolated from patients, we reveal the natural selection mechanisms of SARS-CoV-2 evolution and accurately forecast emerging SARS-CoV-2 variants.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1396
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