Mixture modeling of regularization terms with optimization sampling strategies and its application to biological large scale data

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330330
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Life / Health / Medical informatics
• Research Institution: Kyushu University
• Principal Investigator: Maruyama Osamu 九州大学, マス・フォア・インダストリ研究所, 准教授 (20282519)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 正則化 / マルコフ連鎖モンテカルロ法 / タンパク質複合体 / ガウス分布 / ベイズ推定 / タンパク質間相互作用 / 教師付き学習 / べき法則

Outline of Final Research Achievements

Based on regularization modeling and Markov chain Monte Carlo algorithms, we have developed methods for the protein complex prediction problem and the Bayes estimation of Gaussian distributions. Especially, for the protein complex prediction problem, we have empirically shown the effectiveness of a regularization term based on the information of mutually exclusive protein-protein interactions. In addition, we have developed a supervised learning algorithm for protein complexes with 2 or 3 components. Furthermore, we have designed a regularization term for controlling overlaps between predicted complexes, and showed that the new method with that regularization term outperforms others.

Free Research Field

バイオインフォマティクス