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Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization

Published on Oct 23, 20123425 Views

Mehmet Gönen

**Motivation:** Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very

MLSB 2012 - Basel

Related categories

BiologyBayesian LearningKernel Methods

Chapter list

Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization00:00
In This Talk00:00
Introduction - 100:24
Introduction - 201:05
Introduction - 301:40
Introduction - 402:45
Materials - 103:51
Materials - 204:17
Materials - 305:22
Materials - 405:38
Materials - 506:35
Materials - 607:03
Materials - 707:35
Materials - 808:38
Earlier Approaches - 109:44
Earlier Approaches - 211:06
Earlier Approaches - 311:36
Earlier Approaches - 412:10
Kernelized Bayesian Matrix Factorization - 112:59
Kernelized Bayesian Matrix Factorization - 214:45
Kernelized Bayesian Matrix Factorization - 315:25
Kernelized Bayesian Matrix Factorization - 415:51
Kernelized Bayesian Matrix Factorization - 516:32
Kernelized Bayesian Matrix Factorization - 616:48
Results - 117:09
Results - 217:36
Results - 318:48
Results - 419:35
Results - 520:08
Results - 620:57
Results - 721:50
Conclusions - 122:22
Conclusions - 222:50