POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions
Author(s) -
Shuichi Hirose,
Kana Shimizu,
Satoru Kanai,
Yutaka Kuroda,
Tamotsu Noguchi
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm302
Subject(s) - support vector machine , proteome , computational biology , computer science , intrinsically disordered proteins , artificial intelligence , correlation , biology , machine learning , bioinformatics , mathematics , biophysics , geometry
Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom