NDLI: Supervised Dimensionality Reduction Method for Predicting Membrane Proteins Types

Content Provider	IEEE Xplore Digital Library
Author	Tong Wang Tian Xia Xiao-Ming Hu
Copyright Year	2010
Abstract	With the rapid increase of protein sequences in the post-genomic age, the need for an automated and accurate tool to predict membrane protein types becomes increasingly important. Many efforts have been tried. Most of them aim to find the optimal classification scheme and less of them take the simplifying the complexity of biological system into consideration. This work shows how to decrease the complexity of biological system with the supervised DR (Dimensionality Reduction) method by transforming the original high-dimensional feature vectors into the low-dimensional feature vectors. Moreover, a powerful sequence encoding scheme by fusing PSSM (Position-Specific Score Matrix) and PseAA (Pseudo Amino Acid) method is used to represent the protein samples. Then, the K-NN (K-Nearest Neighbor) classifier is employed to identify the membrane protein types based on their reduced low-dimensional feature vectors. As a result, the jackknife and re-substitution test success rates on this model reach 85.2% and 92.6% respectively, and suggesting that the proposed approach is very promising for predicting membrane proteins types.
Starting Page	112
Ending Page	115
File Size	452034
Page Count	4
File Format	PDF
ISBN	9781424478699
DOI	10.1109/IHMSC.2010.127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machine classification Biological systems Amino acids Feature extraction Biomembranes Vectors
Content Type	Text
Resource Type	Article

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