BEGIN:VCALENDAR
PRODID:-//Compnay Inc//Product Application//EN
VERSION:2.0
METHOD:PUBLISH
BEGIN:VEVENT
DTSTART:20260414T093000Z
DTEND:20260414T103000Z
DTSTAMP:20260419T083027Z
UID:a0c1c2b0-c533-432f-9160-516f4d280f63
CREATED:20260410T114547Z
X-ALT-DESC;FMTTYPE=text/html:Castle.Proxies.LanguageContentMapProxy
DESCRIPTION:Abstract 
The vastness of genomic sequence space and the heterogeneity of mutational data require efficient computational frameworks to extract meaningful biological insights. In this talk, I present two complementary approaches addressing this challenge.First, I introduce cgNA+, a physics-informed machine learning model of nucleic acids trained on atomistic molecular dynamics simulations. It near-instantaneously predicts sequence-dependent DNA structural properties, enabling genome-scale ...
LAST-MODIFIED:20260419T083027Z
LOCATION:Online (Zoom meeting) zoom meeting details: Zoom link: https://zoom.us/j/95229199186?pwd=SUyIpc4yolNtYqZeKpHaR1dxnA9m8A.1
Meeting ID: 952 2919 9186; Passcode: 621708...
SEQUENCE:0
STATUS:CONFIRMED
SUMMARY:Genome Structure and Physics-Informed Machine Learning
TRANSP:OPAQUE
END:VEVENT
END:VCALENDAR