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Learning a Markov logic network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes
Published on Oct 23, 20123158 Views
**Motivation:** Gene regulatory network inference remains a challenging problem in systems biology despite numerous approaches. When substantial knowledge on a gene regulatory network is already ava
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Chapter list
Learning a Markov logic network for supervised gene regulation inference00:00
Switch proliferation/differentiation of skin primary cells - 100:10
Switch proliferation/differentiation of skin primary cells - 200:55
Goal of the study02:26
Machine Learning for biological network inference02:45
Modeling/predicting edges in the graph03:28
Our approach: learning a Markov Logic Network04:06
Outline 06:28
Using first order logic to encode data06:42
Predicates encoding experimental data and prior knowledge08:16
Structure of a small Markov Logic Network (example)10:13
Markov Logic Network (MLN)11:09
MLN for a supervised prediction of a regulation12:26
Modeling the posterior probability of a regulation between i and j 13:14
Discriminative learning of weights given the structure14:39
Discriminative learning of the structure15:52
Description of the experimental studies16:58
Comparison using a baseline pairwise SVM18:19
Averaged cross-validation measurements on balanced samples (1)20:16
Averaged cross-validation measurements on balanced samples (2)21:03
Network completion with a new set of genes (1)21:28
Conclusion and perspectives - 126:08
Conclusion and perspectives - 227:21
Network completion with a new set of genes (2)27:42
And what about the rules ?28:46