Introduction to machine learning

• C.M.Bishop. Pattern recognition and Machine Learning. Chapter 8.
• M.I. Jordan. Graphical models. Statistical Science 19(1):140-155, 2004.
• University of Cambridge Basic Machine Learning course

Introduction to molecular biology and bioinformatics

• Kuo, W.P. et al. . A primer on gene expression and microarrays for machine learning researchers. Journal of Biomedical Informatics, 37(4):293-303, 2004.
• Zien, A. A primer on molecular biology.

Reviews

• Madeira, S. and Oliveira, A. Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE Tr. CCB 1(1):24-45, 2004.
• Segal, E. et al. From signatures to modules: Understanding cancer using microarrays. Nature Genetics 37(suppl):S38-S45, 2005. 
• Ideker, T. and Sharan R. Protein networks in disease. Genome Research 18(4):644-652, 2008.

• Schmidt, M. et al. Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. AISTATS, 2009.
• Friedman, J. et al. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3): 432-441, 2007.
• Lee, S.-I. et al. Eficient structure learning of markov networks using l1-regularization. NIPS, 2006.
• Elidan, G. et al. "Ideal parent" structure learning for continuous variable Bayesian networks. Journal of Machine Learning Research 8:1799-1833, 2007.