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SUMMARY:IC Colloquium: Machine learning approaches to human disease
DTSTART:20230123T093000
DTEND:20230123T103000
DTSTAMP:20260407T202719Z
UID:dab5efd0f318fc3fd3a5c05a53903daf7b4ec1c1eb66f7a7a57ca00e
CATEGORIES:Conferences - Seminars
DESCRIPTION:By: Michael Skinnider - University of British Columbia\nIC/SV 
 Faculty candidate\n\nAbstract\nAdvances in genomic\, transcriptomic\, and 
 proteomic technologies now enable the routine measurement of thousands of
  biomolecules within any biological sample. With the advent of single-cel
 l techniques that multiplex these measurements over millions of cells\, th
 e scale of biological data generation is outpacing our human ability to c
 omprehend this data. Approaches based on machine intelligence offer the p
 ossibility of augmenting our human capacity for understanding in order to
  enable biological discovery. In this seminar\, I will explore application
 s of machine learning to enable biological discovery from genomic\, prote
 omic\, transcriptomic\, and metabolomic datasets\, and discuss implicatio
 ns for the diagnosis and treatment of human disease. Areas of focus will 
 include the discovery of novel antibiotics from nature using genomic and 
 metabolomic datasets\; charting protein-protein interactions within mammal
 ian tissues and across the tree of life using proteomic datasets\; mappin
 g neural circuits in high-throughput using single-cell and spatial transcr
 iptomics datasets\; and the identification of novel designer drugs of abus
 e from metabolomic datasets. Looking forward\, I will explore how machine
  learning might bridge the most significant remaining gap in our ability 
 to measure biological systems: namely\, our ability to measure all of the
  small molecules within any biological sample. I will discuss the use of 
 chemical artificial intelligence to identify unknown molecules within mass
  spectrometry data\, and the implications of this technology for our unde
 rstanding of cancer metabolism and germline cancer risk.\n\nBio\nMichael 
 Skinnider is an MD/PhD student at the University of British Columbia and a
  visiting PhD student at EPFL. Previously\, he earned an Bachelor’s of 
 Arts & Science from McMaster University. His undergraduate research\, wit
 h Nathan Magarvey\, leveraged bacterial genomes and metabolomes to discove
 r new antibiotics from nature. Some of the technology he developed as an 
 undergraduate is now being licensed by a successful spin-off company\, Ad
 apsyn Bioscience. His doctoral research\, with Leonard Foster\, focused on
  the use of proteomics to map cellular protein interaction networks. Sepa
 rately\, his work with Grégoire Courtine has employed single-cell and sp
 atial transcriptomics to mechanistically dissect the recovery of walking 
 after paralysis. More recently\, he has developed chemical AI to automate 
 the discovery of new small molecules\, including human metabolites and de
 signer drugs of abuse (with David Wishart and Petur Dalsgaard). He is ori
 ginally from Victoria\, British Columbia. His research interests are broad
 ly at the intersection of applied machine learning for problems in biolog
 y\, chemistry\, and medicine\, with a particular interest in the discovery
  of novel small molecules.\n\nMore information
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420 https://epfl.zoom.us/
 j/61277011222
STATUS:CONFIRMED
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