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 Lettjournal_title
Protein and peptide lettersauthors
Wang D,Yang L,Fu Z,Xia Jdoi
10.2174/092986611795446085subject
Has Abstractpub_date
2011-07-01 00:00:00pages
684-9issue
7eissn
0929-8665issn
1875-5305pii
BSP/ PPL/ E pub/0301journal_volume
18pub_type
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