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Multiple linear regression for protein secondary structure prediction
Author(s) -
Pan XianMing
Publication year - 2001
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.1036
Subject(s) - jackknife resampling , protein secondary structure , sequence (biology) , regression , linear regression , mathematics , computer science , statistics , pattern recognition (psychology) , artificial intelligence , biology , genetics , biochemistry , estimator
In the present work, a novel method was proposed for prediction of secondary structure. Over a database of 396 proteins (CB396) with a three‐state‐defining secondary structure, this method with jackknife procedure achieved an accuracy of 68.8% and SOV score of 71.4% using single sequence and an accuracy of 73.7% and SOV score of 77.3% using multiple sequence alignments. Combination of this method with DSC, PHD, PREDATOR, and NNSSP gives Q 3 = 76.2% and SOV = 79.8%. Proteins 2001;43:256–259. © 2001 Wiley‐Liss, Inc.
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