NDLI: Recognition of beta-alpha-beta motifs in proteins by using Random Forest algorithm

Content Provider	IEEE Xplore Digital Library
Author	Lixia Sun Xiuzhen Hu
Copyright Year	2013
Description	Author affiliation: Coll. of Sci., Inner Mongolia Univ. of Technol., Hohhot, China (Lixia Sun; Xiuzhen Hu)
Abstract	A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A Random Forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	546
Ending Page	551
File Size	339350
Page Count	6
File Format	PDF
e-ISBN	9781479927616
DOI	10.1109/BMEI.2013.6747001
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Decision support systems Radio frequency Hydropathy component of position Complex super-secondary structure Tin Structure prediction Random Forest algorithm Amino acids Prediction algorithms Vectors
Content Type	Text
Resource Type	Article

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