| Project/Area Number |
26330265
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kwansei Gakuin University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
岡田 孝 関西学院大学, 理工学部, 理工学部研究員 (00103135)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | データマイニング / 列挙アルゴリズム / 有機化合物 / 医薬品 |
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| Outline of Final Research Achievements |
A graph is one of the most natural data structures for representing structured data. For instance, a chemical compound is represented as a graph, where each vertex corresponds to an atom, each edge corresponds to a bond between two atoms therein, and the label of the vertex corresponds to an atom type. Recently, the topic of graph mining has become of great interest because knowledge discovery from structured data can be applied to various real-world datasets.
In this work, we proposed the following three algorithms by representing chemical molecules as labeled graphs: (1) an algorithm for enumerating a complete set of labeled graphs efficiently each of which output in 13 microseconds in average, (2) an algorithm for searching labeled graphs of which q is a supergraph, when given a set of labeled graphs G and a query graph q, and (3) an algorithm for learning the relationship between labeled graphs and their properties and for forecasting a property of a new labeled graph.
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