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UID:1ee24c0d26484e34c511fad971af8f9a
CATEGORIES:Mathematical Physics Seminar
CREATED:20241220T033033
SUMMARY:Webinar: Andrea Bertozzi - High-throughput optimization of DNA-aptamer secondary structure for classification and machine learning intepretability
LOCATION:Zoom
DESCRIPTION:MATHEMATICAL PHYSICS WEBINAR\n RUTGERS UNIVERSITY\n________________________
 __________________\nAndrea Bertozzi – UCLA\n \nWednesday, January 15th , 10
 :45AM EST\n \nHigh-throughput optimization of DNA-aptamer secondary structu
 re for classification and machine learning intepretability\n \nWe consider 
 the secondary structures for aptamers, single stranded DNA sequences that o
 ften fold on themselves and can be designed to bind to small molecules. Giv
 en a specific aptamer sequence, there are well-established computational to
 ols to identify the lowest energy secondary structure. However there is nee
 d for a high-throughput process whereby thousands of DNA structures can be 
 calculated in real time for use in an interactive setting, in particular wh
 en combined with aptamer selection processes in which thousands of candidat
 e molecules are screened in the lab. We present a new method called GMfold,
  which algorithmically uses subgraph matching ideas, in which the DNA chain
  is a graph with nucleotides as graph nodes and adjacency along the chain t
 o define edges in the primary DNA structure. This allow us to cluster thous
 ands of DNA strands using modern machine learning algorithms. We present ex
 amples using data from in vitro systematic evolution of ligands by exponent
 ial enrichment (SELEX). This work is intended to serve as a building block 
 for future machine-learning informed DNA-aptamer selection processes for ta
 rget binding and medical therapeutics.\n
X-ALT-DESC;FMTTYPE=text/html:<p style="text-align: center;"><strong>MATHEMATICAL PHYSICS WEBINAR<br> RUT
 GERS UNIVERSITY</strong></p><p style="text-align: center;"><strong>________
 __________________________________</strong></p><p style="text-align: center
 ;"><strong>Andrea Bertozzi – UCLA</strong></p><p style="text-align: center;
 "><strong>&nbsp;</strong></p><p style="text-align: center;"><strong>Wednesd
 ay,&nbsp;January 15th ,&nbsp;10:45AM EST</strong></p><p style="text-align: 
 center;"><strong>&nbsp;</strong></p><p style="text-align: center;"><strong>
 High-throughput optimization of DNA-aptamer secondary structure for classif
 ication and machine learning intepretability</strong></p><p style="text-ali
 gn: center;"><strong>&nbsp;</strong></p><p>We consider the secondary struct
 ures for aptamers, single stranded DNA sequences that often fold on themsel
 ves and can be designed to bind to small molecules. Given a specific aptame
 r sequence, there are well-established computational tools to identify the 
 lowest energy secondary structure. However there is need for a high-through
 put process whereby thousands of DNA structures can be calculated in real t
 ime for use in an interactive setting, in particular when combined with apt
 amer selection processes in which thousands of candidate molecules are scre
 ened in the lab. We present a new method called GMfold, which algorithmical
 ly uses subgraph matching ideas, in which the DNA chain is a graph with nuc
 leotides as graph nodes and adjacency along the chain to define edges in th
 e primary DNA structure. This allow us to cluster thousands of DNA strands 
 using modern machine learning algorithms. We present examples using data fr
 om in vitro systematic evolution of ligands by exponential enrichment (SELE
 X). This work is intended to serve as a building block for future machine-l
 earning informed DNA-aptamer selection processes for target binding and med
 ical therapeutics.</p>
DTSTAMP:20260828T185711
DTSTART;TZID=America/New_York:20250115T104500
DTEND;TZID=America/New_York:20250115T120000
SEQUENCE:0
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