人工知能
Online ISSN : 2435-8614
Print ISSN : 2188-2266
人工知能学会誌(1986~2013, Print ISSN:0912-8085)
概念階層と探索バイアスを用いたILP手法によるタンパク質機能モデルの発見
石川 孝美宅 成樹寺野 隆雄
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解説誌・一般情報誌 フリー

2000 年 15 巻 1 号 p. 169-176

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The paper describes machine discovery of protein functional models from protein databases with an Inductive Logic Programming (ILP) method using conceptual hierarchy and search biases. The proposed ILP method discovers effectively protein functional models that explain the relationship between protein functions and structures from protein databases describing amino acid sequences and properties of proteins. The method is based on top-down search for relative least general generalization and uses domain knowledge defining the conceptual hierarchy of protein functions and search biases. The method succeeds in discovering protein functional models for forty membrane proteins, which coincide with conjectured models literature of molecular biology.

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© 2000 人工知能学会
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