Genome Informatics
Online ISSN : 2185-842X
Print ISSN : 0919-9454
ISSN-L : 0919-9454
Building a Knowledge-Base for Protein Function Prediction using Multistrategy Learning
石川 孝美宅 成樹寺野 隆雄広川 貴次諏訪 牧子謝 文清
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ジャーナル フリー

1995 年 6 巻 p. 39-48

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抄録
Conventional techniques for protein function prediction using similarities of amino acid sequences enable us to only classify the protein functions into function groups. They usually fail to predict specific protein functions. To overcome the limitation, in this paper, we propose a method for protein function prediction using functional feature analysis and a multistrategy learning approach to building the knowledge-base. By “functional feature”, we mean a feature of an amino acid sequence characterizing the function of a protein with the amino acid sequence. They are secondary and/or tertiary structures of amino acid sequences that corresponds to functional elements comprising the functions of a protein. The functional features are extracted from amino acid sequences using Abductive inference, Inductive inference, and Deductive inference. In this paper, we show the effectiveness of the method by an example problem to classify functions of bacteriorhodopsin-like proteins.
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© Japanese Society for Bioinformatics
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