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BMCBI
2008

Functional discrimination of membrane proteins using machine learning techniques

9 years 11 months ago
Functional discrimination of membrane proteins using machine learning techniques
Background: Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results: We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network metho...
M. Michael Gromiha, Yukimitsu Yabuki
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors M. Michael Gromiha, Yukimitsu Yabuki
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