Prediction of thermophilic protein with pseudo amino Acid composition: an approach from combined feature selection and reduction.

Abstract:

:Prediction of thermophilic and mesophilic protein plays a crucial role in both biochemistry and bioengineering. In this study, a different mode of pseudo amino acid composition (PseAAC) was proposed to formulate the protein samples by integrating the amino acid composition, the physic chemical features, as well as the composition transition and distribution features, where each of the protein samples was represented by a numerical vector through the sequence-based approach. Using the support vector machine algorithm, an accurate and reliable classifier was constructed to predict the thermophilic and mesophilic proteins. Moreover, three feature reduction algorithms were obtained for locating the most vital features and reducing the size of feature space. Among the three feature reduction algorithms, the genetic algorithm performed best. Finally, with the reduced features extracted from the genetic algorithm, it was observed that for the selected dataset the new classifier achieved a high accuracy of 95.93% with the Matthews correlation coefficient of 0.9187.

journal_name

Protein Pept Lett

authors

Wang D,Yang L,Fu Z,Xia J

doi

10.2174/092986611795446085

subject

Has Abstract

pub_date

2011-07-01 00:00:00

pages

684-9

issue

7

eissn

0929-8665

issn

1875-5305

pii

BSP/ PPL/ E pub/0301

journal_volume

18

pub_type

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