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DTSTART:19700308T020000
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DTSTAMP:20190719T085744Z
LOCATION:HG F 3
DTSTART;TZID=Europe/Stockholm:20190613T124500
DTEND;TZID=Europe/Stockholm:20190613T131500
UID:submissions.pasc-conference.org_PASC19_sess120_msa321@linklings.com
SUMMARY:Revisiting Motif Trees: High Performance White Box Predictors for 
 Biological Sequences Modeling
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Emerg
 ing Application Domains, Chemistry and Materials, Climate and Weather, Phy
 sics, Solid Earth Dynamics, Life Sciences, Engineering\n\nRevisiting Motif
  Trees: High Performance White Box Predictors for Biological Sequences Mod
 eling\n\nFalcone, Charpilloz\n\nDue to the constant improvement of sequenc
 ing methods and equipment, biological databases are increasing exponential
 ly. In order to exploit such amounts of data, machine learning has been ro
 utinely used in bioinformatics during the last 25 years. However, most pre
 vious research did focus on building black box sequence predictors. While 
 very useful to analyze and sort raw biological data, these approaches did 
 not improve much biological comprehension. In this talk, we present a new 
 method for modeling sequence consensus based on decision trees and languag
 e theory. Resulting classifiers allow not only to predict sequence pattern
 s but also to visualise pattern structure and requirements. As an example,
  we will show how to build an efficient model for the N-terminal acetylati
 on of proteins, which is the most common post translational modification i
 n eukaryotic cells.
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