Multistrategy Learning Model Based on Analogical Reasoning and its Application to Function Prediction of Proteins
| Project/Area Number |
07680381
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tsukuba |
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Principal Investigator |
TERANO Takao The University of Tsukuba, Institute of Policy and Planning Sciences, Professor, 社会工学系, 教授 (20227523)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIKAWA Takashi Kisarazu National College of Technology, Deptartment of Informaion Engineering,, 情報工学科, 助教授 (40270227)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1996: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1995: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Artificial Intelligence / Machine Learning / Multistrategy Learning / Analogical Reasoning / Genome Informatics / Function Prediction of Proteins / Amino Acid Sequence Data / Inductive Learning |
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| Research Abstract |
In this project, we present a novel machine learning technique in a logic programming environment : Inductive Prediction by Analogy (IPA). IPA learns the description of a target predicate similar to a source predicate from examples of the target predicate. A key feature of IPA is that it uses analogies to constrain the hypothesis space using taxonomic information represented by first-order predicate logic.
Typical problems addressed by IPA are to decide whether a given ground atom is valid or not, when no concept descriptions for the goal are available in a knowledge base. This is attained by the steps : 1) recognition of a candidate analogous source, 2) elaboration of an analogical mapping between source and target domains, 3) evaluation of mapping and inferences to given examples of the target predicate, and 4) consolidation of the outcome of the analogy.
To validate the effective of the proposed method, we apply it to build a knowledge-base for protein function prediction. Conventional techniques for the 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, we utilize IPA for functional feature analysis. 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 the sequences that corresponds to functional elements comprising the functions of a protein. We have shown the effectiveness of IPA by applying it to classifying functions of a bacteriorhodopsin-like family of proteins.
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Report
(3 results)
Research Products
(30 results)
