The 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)

Context-specific Independence Mixture Modelling for Protein Families

author: Benjamin Georgi, Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics

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*Slides*
0:00	Context-specific Independence Mixture Modelling for Protein Families
0:01	Introduction
0:20	Functional Positions
1:16	Introduction
2:16	Context-specific independence mixture models
2:23	Mixture Models (1)
2:52	Mixture Models (2)
3:38	Mixture Models (3)
3:52	Mixture Models (4)
4:13	CSI Mixture Models (1)
5:51	CSI Mixture Models (2)
6:02	CSI for Protein Families (1)
6:21	CSI for Protein Families (2)
6:47	CSI for Protein Families (3)
7:05	Prediction of Functional Residues
7:58	CSI mixture structure learning
8:07	CSI Structure Learning (1)
8:17	CSI Structure Learning (2)
8:18	CSI Structure Learning (3)
8:25	CSI Structure Learning (4)
8:31	CSI Structure Learning (5)
8:48	CSI Structure Learning (6)
9:02	Learning Algorithm
9:39	CSI mixtures for protein data
9:52	Mixtures for Proteins
10:51	Conceptional problem
11:35	Amino acid properties (1)
11:51	Amino acid properties (2)
12:05	Model extension
12:31	Dirichlet Distribution
13:54	Dirichlet Mixture Priors (DMP)
14:40	DMP for Amino acids (1)
14:53	DMP for Amino acids (2)
15:35	DMP for Amino acids (3)
16:00	Results
16:01	Data sets
16:27	Malate / Lactate Dehydrogenase (1)
16:47	Malate / Lactate Dehydrogenase (2)
17:14	MDH/LDH (1)
17:29	MDH/LDH (2)
17:40	MDH/LDH (3)
17:58	Guanylyl / Adenylyl Cyclases (1)
18:12	Guanylyl / Adenylyl Cyclases (2)
18:41	AC/GC Cyclases (1)
18:46	AC/GC Cyclases (2)
19:06	AC/GC Cyclases (3)
19:21	AC/GC Cyclases (4)
19:33	Conclusion
20:27	Future work
20:27	Software
20:36	Thank you.
21:52	- Questions

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