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SUMMARY:A White-Box Machine Learning Approach for Revealing Pathway Mechan
 isms
DTSTART:20200122T110000
DTEND:20200122T120000
DTSTAMP:20260407T042256Z
UID:f36caf0010983f4c2b6df48b21c56fa20d707195fb26586896e4c117
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Jason H. Yang\, Rutgers New Jersey Medical School\, Newa
 rk\, NJ (USA)\nBIOENGINEERING SEMINAR\n \nAbstract:\nRecent advances in h
 igh-throughput experimental technologies and data analyses now enable unpr
 ecedented observation\, quantification and association of biological signa
 ls with cellular phenotypes. However\, current approaches for interpreting
  large biomedical datasets are unable to provide casual\, mechanistic biol
 ogical understanding. Here\, we will describe a “white-box” machine le
 arning approach integrating prospective cellular network modeling with mac
 hine learning to identify experimentally testable pathway mechanisms from 
 biochemical screening data.\n\nWe will demonstrate how this approach enabl
 ed the novel discovery that purine biosynthesis is involved in bactericida
 l antibiotic lethality\, through its coupling to central carbon metabolism
 . We will discuss how such approaches may be extended towards advancing sy
 stems medicine by revealing mechanisms underlying disease pathogenesis and
  therapeutic efficacy. We propose such approaches may be generalized to in
 vestigate any quantifiable cellular phenotype using relevant biological ne
 tworks.\n\nBio:\nDr. Jason Yang is a new Assistant Professor and Chancello
 r’s Scholar in the Department of Microbiology\, Biochemistry and Molecul
 ar Genetics and in the Center for Emerging and Re-Emerging Pathogens at Ru
 tgers New Jersey Medical School. He received his Ph.D. in Biomedical Engin
 eering from the University of Virginia\, where he trained with Dr. Jeffrey
  Saucerman\, studying β-adrenergic signaling in cardiac myocytes. He comp
 leted his postdoctoral training with Dr. James Collins at MIT and the Broa
 d Institute\, studying metabolic mechanisms of antibiotic-induced bacteria
 l death physiology. Jason leads a systems biology research group\, where t
 hey are developing approaches that integrate high-throughput experimentati
 on with network modeling and machine learning to accelerate the discovery 
 of causal biological mechanisms as they pertain to the pathogenesis and tr
 eatment of chronic and infectious diseases.
LOCATION:BM 5202 https://plan.epfl.ch/?room==BM%205202
STATUS:CONFIRMED
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