2013 Fiscal Year Final Research Report
Algorithms for prediction of MHC binders using mathematical optimization models
| Project/Area Number |
23510152
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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 |
Social systems engineering/Safety system
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| Research Institution | Tokyo University of Science (2013)
Muroran Institute of Technology (2011-2012) |
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Principal Investigator |
SHI Jianming 東京理科大学, 経営学部, 教授 (70287465)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 分数計和計画問題 / 数理最適化 / アルゴリズム / 遺伝子機能予測 / 主要組織適合抗原(MHC)クラスII |
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| Research Abstract |
Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules plays an important role in vaccine development. A key to such prediction is to maximize the entropy of information obtained from the the binding ability. This problem can be formulated as a maximization of the Sum of Ratios Problem. In this study, two kinds of algorithms which use linear relaxation were proposed to such a Sum of Linear Ratios Problem, that is an approximation of general Sum of Ratios Problem. The following results have been obtained in the numerical experiments.
1) The new algorithm takes a 12% CPU time of the existing methods on average. 2) The proposed algorithm finds an optimal solution of the Sum of Linear Ratios Problem with 60 ratios in about less than 14 minutes (CPU time).
These numerical results indicate that the proposed algorithms are much superior to theses existing algorithms.
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