生物機器學習

This course is devoted to introducing and developing some effective and efficient machine learning techniques for analyzing several important computational biology problems, such as protein-DNA binding, microarray analysis of gene expression, and bioimage informatics. At the same time, novel machine learning methods are presented and analyzed. The analysis should help shed some light on this new and exciting area, and should be especially useful to professionals in Bioinformatics and Machine Learning fields. The major topics are as follows: Introduction to machine learning Computational Methodologies .k-Nearest neighbor .Decision trees .Artificial neural networks .Support vector machines (SVM) .Support vector regression (SVR) Roles of machine learning in: .Artificial intelligence .Image processing .Pattern recognition .Simulation and modeling Tool: .Inheritable bi-objective combinatorial genetic algorithm (IBCGA) .Study and analysis of IBCGA .Case study of IBCGA Applications: Life and medical sciences .Gene network . Protein-DNA binding . Microarray data analysis . Molecular Bioimaging

引導同學搜尋網路學術公布之生物資料庫,應用於課堂提供的機器學習工具進行課程,學習應用操作與結果分析,以E3學習平台作為存放執行程式與繳交結果

課堂講授、操作、討論及報告 課堂參與40%,期中報告30%,期末報告30%

1. Haifeng Li, Applications of Machine Learning Techniques to Bioinformatics, VDM Verlag, ISBN 3639054407, 2008. 2. S. Mitra, S. Datta, T. Perkins and G. Michailidis, Introduction to Machine Learning and Bioinformatics, New York: Chapman & Hall/CRC Press, ISBN 978-1584886822, 2008. 3. Analysis of biological data, edited by S. Bandyopadhyay, U. Maulik and J. T. L. Wang, World Scientific, 2007.