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Multi-task learning in the analysis of phenotypic data
Published on Jun 28, 201984 Views
Multi-task learning is an efficient approach of machine learning which combines data from several related tasks, improving accuracy compared to solving each task separately. With the special requireme
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Chapter list
Multi-task learning in the analysis of phenotypic data00:00
Overview00:08
Multi-task learning00:57
Motivation01:03
Recommender systems - 104:20
Recommender systems - 205:45
Matrix Factorization - 106:36
Matrix Factorization - 206:41
Matrix Factorization - 308:22
Results on Netflix challenge09:15
Matrix Factorization - 410:24
Cold start problem10:29
Matrix factorization with side information - 110:38
Matrix factorization with side information - 211:30
Beyond 2-way relations12:17
Matrix Factorization - 513:38
Probabilistic Model13:56
Matrix Factorization - 616:45
Deep Learning17:08
Embedding and one-hot encoding18:34
Deep factorization model21:26
Application: Chemogenomics22:10
Chemogenomics22:16
Series effect22:57
Evaluation results25:31
Application: Binding mode27:53
Affinity and potency27:57
Competitive vs. non-competitive inhibition28:34
Identification of binding mode30:30
The setup31:19
Latent dimensions for IC50 and Ki32:17
Predicting interaction of pairs33:08
Predicting dominant behaviour33:49
Possible future work35:29
Cell Chemical Biology36:26
Classical high-content imaging36:54
Predicting unrelated protein assays37:29
Assay preparation38:23
Machine Learning Approaches39:14
Experimental Results39:25
In vitro validation40:52
Combining Multiple cell lines - 142:32
Combining Multiple cell lines - 243:02
Number of well predicted targets43:22
Significant differences in recognized assays44:18
Data fusion results45:31
Discussion and future work46:30
Scientific reports48:17
Computational setup48:51
Possible questions50:27
Feature interaction analysis - 151:11
Feature interaction analysis - 252:34
Packages53:41
Thank you fo your attention!56:40