Machine learning in computational and systems biology

Jean-Philippe Vert

Malaysia Genome Institute
November 8-12, 2010

In this 5-day workshop, we introduce several machine learning techniques and illustrate their use in a variety of applications in computational and systems biology. We emphasize in particular support vector machines (SVM) and kernel methods. Applications include the classification of biological sequences, small molecules or microarray data, as well as de novo and supervised reconstruction of biological networks.



Monday 8/11Lecture: Machine learning, SVM, kernelsR basics (P0). Linear SVM (P1).
Tuesday 9/11Cross-validation, parameters selection (P1)Nonlinear SVM, gene expression classification (P1).
Wednesday, 10/11String kernels (P3).Protein annotation with string kernels (P3)
Thursday, 11/11Reconstruction of regulatory network (P4)Reconstruction of regulatory network(P4)
Friday, 12/11Reconstruction of PPI and metabolic network (P5)Free

Practical sessions


The practical sessions require the following free softwares:

P0: R basics

P1: SVM and kernel methods basics

P2: Using your own kernels

P3: Classification of sequences with string kernels

P4: Reconstruction of regulatory networks from expression

P5: Reconstruction of PPI and metabolic networks

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