Week 16:

Tuesday, 5/1
Ong, CS and Zien, A. An Automated Combination of Kernels for Predicting Protein Subcellular Localization. In: Proceedings of the 8th Workshop on Algorithms in Bioinformatics (WABI), pp. 186-179, Springer. Lecture Notes in Bioinformatics.

Thursday, 5/3
No class - we will meet during finals week for class presentations instead.

Week 15:

Tuesday, 4/24
We will continue the discussion of the Thursday paper.

Thursday, 4/26
Borgwardt K.M., Ong C.S., Schönauer S., Vishwanathan S.V.N., Smola A.J., Kriegel H.-P. Protein Function Prediction via Graph Kernels. Intelligent Systems in Molecular Biology" (ISMB 2005), Detroit, USA, 2005 and Bioinformatics 2005 21(suppl_1):i47-i56.

Week 14:

Tuesday, 4/17
G. Schweikert, C. Widmer, B. Schölkopf et al.An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. In: Advances in Neural Information Processing Systems (NIPS) 21, 2009.

Thursday, 4/19
P. Kuksa, P.H. Huang, and V. Pavlovic. Efficient use of unlabeled data for protein sequence classification: a comparative study.

Week 13:

Tuesday, 4/10
Read chapters 2 and 6 in: Xiaojin Zhu and Andrew B. Goldberg. Introduction to Semi-Supervised Learning. Morgan & Claypool, 2009.

Thursday, 4/12
Michael Hamilton, A.S.N. Reddy, and Asa Ben-Hur. Towards a plant splicing code: conserved splicing regulatory elements from SVM-weighted features.

Week 12:

Tuesday, 4/3
A. Ben-Hur and W.S. Noble. Kernel methods for predicting protein-protein interactions. In: Proceedings, thirteenth international conference on intelligent systems for molecular biology. Bioinformatics 21(Suppl. 1): i38-i46, 2005.

Thursday, 4/5
F. ul Amir Afsar Minhas and A. Ben-Hur. Multiple instance learning of Calmodulin binding sites. Submitted.

Week 11:

Tuesday, 3/27
Wei Bi and James Kwok. Multi-Label Classification on Tree- and DAG-Structured Hierarchies. International Conference on Machine Learning (ICML-11), 2011. (Indika)

Thursday, 3/29
We will continue to discuss the paper
A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Week 10:

Tuesday, 3/20
Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net. JRSSB (2005) 67(2) 301-320. An R package elasticnet is available from CRAN. (Majdi)
Feiping Nie, Heng Huang, Cai Xiao, Chris Ding. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization. NIPS 2010. (Rehab)

Thursday, 3/22
Y. Sun, S. Todorovic, and S. Goodison. Local Learning Based Feature Selection for High Dimensional Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010. (Nand)

Week 9:

Spring break

Week 8:

Tuesday, 3/6
Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005. (Joel)
Yeung K, Bumgarner R. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology. 4:R83, 2003. (Simon)

Thursday, 3/8
Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information Processing Systems (NIPS) 16, 2004. 1-norm classifiers create very sparse representations, so are useful for feature selection. (Prathamesh)

Week 7:

Tuesday, 2/28
Lecture: Discussion of evaluation of feature selection; model selection; more discussion of kernels
Thursday, 3/1
Lecture: Protein function prediction.
Reading: A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Week 6:

Tuesday, 2/21
Lecture: Embedded feature selection methods (from the notes) and discussion of experimental issues in feature selection and model selection.
Reading: C Ambroise and GJ McLachlan. Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS 99 (10) 6562-6566, 2002.

Thursday, 2/23
Lecture: Discussion of NIPS 2003 feature selection competition.
Reading: I. Guyon, S.R. Gunn, A. Ben-Hur and G. Dror. Results analysis of the NIPS 2003 feature selection challenge. Advances in Neural Information Processing Systems 17, 2004.

Week 5:

Tuesday, 2/14
Lecture: SVMs and SVM training algorithms, SVM demo.

Thursday, 2/16
Lecture: Feature selection [ notes ]
Reading: Isabelle Guyon, André Elisseeff. An Introduction to variable and feature selection. Journal of Machine Learning Research, 3(7-8), 2003.

Week 4:

Tuesday, 2/7
Lecture: Maximum margin classifiers and constrained optimization problems.
Reading: Chapter 7 in Learning with Kernels.

Thursday, 2/9
Lecture: Maximum margin classifiers (cont).
Reading: Chapter 7 in Learning with Kernels.

Week 3:

Tuesday, 1/31
Lecture: Demo of PyML.

Tuesday, 2/2
Lecture: Maximum margin classifiers.
Reading: Chapter 7 in Learning with Kernels.

Week 2:

Tuesday, 1/24
Lecture: Linear classifiers and kernels [ notes ]
Reading: Sections 1.1 and 1.2 in Learning with Kernels or chapter 2 in Kernel methods for pattern analysis.

Thursday, 1/26
Lecture: Linear classifiers and kernels (continued).

Week 1:

Tuesday, 1/17
Lecture: Course introduction - some biology basics, the role of machine learning in bioinformatics [ slides ]
Reading: Alex Zien's A primer on molecular biology [ pdf ].
Martin Tompa's notes [ pdf ].
Thursday, 1/19
Lecture: Course introduction - continued.