A hydrophobicity based neural network method for predicting transmembrane segments in protein sequences

Author

Chen, Zhongqiang ; Liu, Qi ; Zhu, Yisheng ; Li, Yixue ; Xu, Yuhong

Author_Institution

Dept. of Bioimedical Eng., Shanghai Jiao Tong Univ., China

Volume

3

fYear

2001

fDate

2001

Firstpage

2899

Abstract

Transmembrane proteins play vital roles in living cells. The difficulties in determining the topology of transmembrane protein experimentally and the increasing amino acid sequence data from genome projects provide great demand for computational methods to predict the region of transmembrane segments in protein sequences. A hydrophobicity based supervised learning vector quantization neural network prediction method is presented. The prediction accuracy is above 90% and comparable to existing methods.

Keywords

biology computing; biomembranes; cellular biophysics; molecular biophysics; neural nets; proteins; vector quantisation; amino acid sequence data; computational methods; genome projects; hydrophobicity based neural network method; prediction accuracy; protein sequences; supervised learning vector quantization neural network prediction method; transmembrane segments prediction; transmembrane segments region; transmembrane topology determination; Accuracy; Amino acids; Bioinformatics; Genomics; Network topology; Neural networks; Prediction methods; Proteins; Supervised learning; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1017396

Filename

1017396