Machine Learning Summer School 2006 - Taipei

Kernel Methods in Computational Biology

author: Jean-Philippe Vert, Ecole des Mines de Paris - Paris Tech

Description

Many problems in computational biology and chemistry can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not vectors. Indeed objects such as gene sequences, small molecules, protein 3D structures or phylogenetic trees, to name just a few, have particular structures which contain relevant information for the statistical problem but can hardly be encoded into finite-dimensional vector representations. Kernel methods are a class of algorithms well suited for such problems. Indeed they extend the applicability of many statistical methods initially designed for vectors to virtually any type of data, without the need for explicit vectorization of the data. The price to pay for this extension to non-vectors is the need to define a positive definite kernel between the objects, formally equivalent to an implicit vectorization of the data.

Categories

Top: Computer Science: Bioinformatics
Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
0:05	Kernel Methods in Bioinformatics
3:41	Outline
4:26	Outline 01
4:46	Outline 02
5:36	Outline 03
6:17	A short introduction to molecular biology
6:47	Short history of genomics
14:34	A cell
16:33	Chromosomes
17:23	Chromosomes and DNA
18:18	Structure of DNA
18:34	The double helix
22:13	Genomes
24:29	Central dogma
28:08	Proteins
31:04	Genetic code
33:34	Human genome project
36:37	2003: End of genomics era
41:07	The post-genomic technological revolution
49:00	Example: DNA microarrays
52:04	Data available
56:34	Expectations
60:02	Some computational challenges
61:53	Some computational challenges 01
63:43	Some computational challenges 02
63:59	Some computational challenges 03
65:22	Summary
68:28	Kernels and Kernel Methods
71:23	Biological data
73:25	Kernel methods for bioinformatics
75:47	Outline

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