6th IARP -TC-15 Workshop on Graphbased Representations in Pattern Recognition

Graph kernels and applications in chemoinformatics

author: Jean-Philippe Vert, MINES ParisTech

Description

Several problems in chemistry can be formulated as classification or regression problems over molecules which, when represented by their planar structure, can be seen as labeled graphs. Several approaches have been proposed recently to define positive definite kernels over labeled graphs, paving the way to the use of powerful kernel methods in chemoinformatics. In this talk I will review some of these approaches and present relevant applications in computational chemistry.

Categories

Top: Computer Science: Machine Learning: Kernel Methods
Top: Computer Science: Chemoinformatics
Top: Computer Science: Machine Learning: Structured data

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*Slides*
0:00	Graph kernels and applications in chemoinformatics
1:07	Outline
2:15	Outline - Introduction
2:17	Ligand-Based Virtual Screening
4:50	Example
5:50	Image retrieval and classification
6:48	Formalization
7:51	Classical approaches
8:33	Classical approaches (2)
9:35	Difficulties
11:04	The kernel trick
11:55	The kernel trick (2)
12:41	Kernel trick example: computing distances in the feature space
13:59	Kernel trick example: computing distances in the feature space (2)
15:33	Positive Definite (p.d.) Kernels
16:42	P.d. kernels are inner products
17:23	Graph kernels
18:26	Summary
18:30	Summary (2)
19:38	Outline - Complexity vs expressiveness trade-off
19:56	Expressiveness vs Complexity
20:42	Expressiveness vs Complexity (2)
21:03	Complexity of complete kernels
21:37	Complexity of complete kernels (2)
21:55	Subgraphs
23:11	Subgraph kernel
24:22	Subgraph kernel complexity
24:40	Subgraph kernel complexity (2)
26:43	Subgraph kernel complexity (3)
28:44	Paths
29:23	Path kernel
29:33	Path kernel (2)
29:43	Path kernel (3)
29:45	Summary
30:08	Outline - Walk kernels
30:12	Walks
30:34	Paths and walks
31:00	Walk kernel
31:45	Walk kernel (2)
32:10	Walk kernel examples
33:14	Walk kernel examples (2)
34:14	Walk kernel examples (3)
35:32	Computation of walk kernels
35:48	Product graph
37:17	Walk kernel and product graph
37:25	Walk kernel and product graph (2)
38:26	Computation of the nth-order walk kernel
40:32	Computation of random and geometric walk kernels
42:42	Outline - Extensions
42:58	Extensions 1: label enrichment
44:47	Extension 2: Non-tottering walk kernel
45:49	Computation of the non-tottering walk kernel
47:52	Extension 2: Subtree kernels
48:38	Example: Tree-like fragments of molecules
49:11	Computation of the subtree kernel
49:54	Outline - Applications
49:59	Chemoinformatics
51:10	Subtree kernels
53:22	Image classification
54:53	Outline - Conclusion
54:54	Conclusion
58:19	References

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