BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Computer Science - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Computer Science
X-ORIGINAL-URL:https://www.cs.jhu.edu
X-WR-CALDESC:Events for Department of Computer Science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210212T100000
DTEND;TZID=America/New_York:20210212T110000
DTSTAMP:20260422T133654
CREATED:20210629T210726Z
LAST-MODIFIED:20210629T210726Z
UID:1962565-1613124000-1613127600@www.cs.jhu.edu
SUMMARY:Yana Bromberg\, Rutgers University – “Computational methods for decoding the DNA blueprints of molecular functionality”
DESCRIPTION:Locationhttps://wse.zoom.us/j/96814823677AbstractPrecision medicine efforts propose leveraging complex molecular and medical data towards a better life. This ambitious objective requires advanced computational solutions. Here\, however\, deeper understanding will not simply diffuse from deeper machine learning\, but from more insight into the details of molecular function and a mastery of applicable computational techniques.My lab’s novel machine learning-based methods predict functional effects of genomic variants and leverage the identified patterns in functional changes to infer individual disease susceptibility. We have optimized our genome-to-disease mapping pipeline to both accommodate compute-resistant biologists and allow for custom variant scoring functions\, feature selection\, and machine learning techniques.��We also built novel computational methods\, including training the first general purpose language model for bacterial short-read DNA sequences\, to be used in high-throughput functional profiling of microbiome data that can further elaborate on health and disease. Our purely computational work motivates new experimentally testable hypothesis regarding the biological mechanisms of disease. It also provides a potential means for earlier prognosis\, more accurate diagnosis\, and the development of better treatments.BioResearch in Yana Bromberg’s lab at Rutgers University is focused on designing machine learning\, network analysis\, and other computational techniques for the molecular functional annotation of genes\, genomes\, and metagenomes in the context of specific environments and diseases. The lab also studies evolution of life’s electron transfer reactions in Earth’s history and as potentially applicable to other planets. Dr. Bromberg received her Bachelor degrees in Biology and Computer Sciences from the State University of New York at Stony Brook and a Ph.D. in Biomedical Informatics from Columbia University. She is currently an Associate Professor at the Department of Biochemistry and Microbiology at Rutgers University. She also holds an adjunct position at the Department of Genetics at Rutgers and is a fellow of the Institute for Advanced Study at the Technical University of Munich\, Germany. Dr. Bromberg is also the vice-president of the Board of Directors of the International Society for Computational Biology.HostSteven SalzbergVideoWatch seminar video.
URL:https://www.cs.jhu.edu/event/yana-bromberg-rutgers-university-computational-methods-for-decoding-the-dna-blueprints-of-molecular-functionality/
END:VEVENT
END:VCALENDAR