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BEGIN:VEVENT
UID:DSC-21107
DTSTART;TZID=Europe/Berlin:20240806T130000
SEQUENCE:1723009095
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20240806T150000
URL:https://www.dresden-science-calendar.de/calendar/en/detail/21107
LOCATION:TUD Materials Science - HAL\, Hallwachsstraße 301069 Dresden
SUMMARY:Corte: Interdisciplinary Application of Data Science\, lessons lear
 ned from protein engineering\, drug Discovery\, and pathology
CLASS:PUBLIC
DESCRIPTION:Speaker: Dennis Della Corte\nInstitute of Speaker: Physics and 
 Astronomy Department\, Brigham Young University\nTopics:\nPhysik\n Locatio
 n:\n  Name: TUD Materials Science - HAL (HAL Bürogebäude - 115)\n  Stree
 t: Hallwachsstraße 3\n  City: 01069 Dresden\n  Phone: \n  Fax: \nDescript
 ion: This presentation highlights interdisciplinary applications of data s
 cience methods across protein engineering\, drug discovery\, and pathology
 . Case studies from protein design will show how computational modeling ac
 celerates the design-build-test cycle. Examples from drug discovery will i
 llustrate using machine learning to extract insights from chemical and bio
 logical data to streamline therapy development. Applications to pathology 
 datasets will demonstrate how data integration and deep learning enable en
 hanced disease diagnosis and biomarker discovery. Common principles and ch
 allenges in applying data science will be discussed\, providing perspectiv
 es into how data science drives scientific innovation in diverse fields.&a
 mp\;#13\; &amp\;#13\; Relevant References&amp\;#13\; A probabilistic view 
 of protein stability\, conformational specificity\, and design.\, Stern JA
 \, Free TJ\, Stern KL\, Gardiner S\, Dalley NA\, Bundy BC\, Price JL\, Win
 gate D\, Della Corte D. Nature Scientific Reports\, 2023 &amp\;#13\; &amp\
 ;#13\; TrIP─Transformer Interatomic Potential Predicts Realistic Energy 
 Surface Using Physical Bias\, Bryce E. Hedelius\, Damon Tingey\, and Denni
 s Della Corte\, Journal of Chemical Theory and Computation\, 2024 &amp\;#
 13\; MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Scr
 eening in Drug Discovery\, Connor J. Morris\, Jacob A. Stern\, Brenden Sta
 rk\, Max Christopherson\, and Dennis Della Corte\, Journal of Chemical Inf
 ormation and Modeling\, 2022&amp\;#13\; Don't fear the artificial intelli
 gence: a systematic review of machine learning for prostate cancer detecti
 on in pathology\, Frewing\, A.\, Gibson\, A. B.\, Robertson\, R.\, Urie\, 
 P. M.\, &amp\;amp\; Della Corte\, D.\, Archives of Pathology &amp\;amp\; 
 Laboratory Medicine\, 2024
DTSTAMP:20260911T143111Z
CREATED:20240608T053956Z
LAST-MODIFIED:20240807T053815Z
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