|開催日時：||2018年9月27日 15：30 ～ 16：15|
|開催場所：||総合研究棟8階大セミナー室 (入口にて内線75615, 75617 or 75618に電話下さい。)|
|所属：||Monash Biomedicine Discovery Institute (BDI), Monash University・
Senior Research Fellow and Group Leader
|演題：||Harnessing the power of machine-learning techniques to addresssequence classification problems in the era of biomedical big data|
Recent advances in high-throughput sequencing have significantly contributed to an ever-increasing gap between the number of gene products (‘proteins’) whose function is well characterized and those for which there is no functional annotation at all. Experimental techniques to determine the protein function are often expensive and time-consuming. Recently, machine-learning (ML) techniques based on statistical learning have provided efficient solutions to challenging problems of sequence classification or functional annotation that were previously considered difficult to address. In this talk, by combining our recent research progress, I will highlight some important developments in the prediction of two representative sequence labeling problems in computational biology, i.e. ‘target substrate labeling’ and ‘active site labeling’, based on the high-dimensional, noisy and redundant information derived from sequences and the 3D structure. I will illustrate how ML methods can extract the predictive power from a variety of features that are derived from different aspects of the data can contribute to the model performance.
|世話人：||〇山口 類 (DNA情報解析分野)
井元 清哉 (健康医療データサイエンス分野)