Machine learning in computational and systems biology
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 8/11||Lecture: Machine learning, SVM, kernels||R basics (P0). Linear SVM (P1).|
|Tuesday 9/11||Cross-validation, parameters selection (P1)||Nonlinear SVM, gene expression classification (P1).|
|Wednesday, 10/11||String kernels (P3).||Protein annotation with string kernels (P3)|
|Thursday, 11/11||Reconstruction of regulatory network (P4)||Reconstruction of regulatory network(P4)|
|Friday, 12/11||Reconstruction of PPI and metabolic network (P5)||Free|
The practical sessions require the following free softwares:
P0: R basics
P1: SVM and kernel methods basics
- Goal: learn and manipulate SVM and kernel PCA, understand how they work on simple data, play with kernels and parameters.
- Application: cancer diagnosis from gene expression data.
- Useful R functions
P2: Using your own kernels
- Goal: Use precomputed kernels, and define your own kernels.
P3: Classification of sequences with string kernels
- Goal: understand and test a few string kernels, for classification of protein and DNA sequences
P4: Reconstruction of regulatory networks from expression
P5: Reconstruction of PPI and metabolic networks
- Goal: understand and test several methods for the prediction of protein-protein interactions and edges in the metabolic network.
- ppimetabo.tar.gz: yeast datasets (PPI and metabolic networks)
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