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Gene regulatory network inference using tree-based ensemble methods
Published on Jul 09, 20133709 Views
One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks. In this talk, we first present a method, called GENIE3, for the
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Chapter list
Gene regulatory network inference with tree-based ensemble methods00:00
Biological networks01:13
Biological network inference02:10
Supervised versus unsupervised network inference03:23
Regulatory network inference04:49
Classification of regulatory network inference methods05:59
Outline07:11
Unsupervised inference of gene regulatory networks07:11
Non-integrative and unsupervised GRN inference07:12
GRN inference: issues07:55
GRN inference: existing methods08:00
GENIE309:08
Network inference as p feature selection problems09:10
Regression trees as predictive models11:00
Ensembles of regression trees as predictive models12:05
Feature ranking using regression tree ensembles13:15
GEne Network Inference with Ensemble of Trees (GENIE3) - 114:57
GEne Network Inference with Ensemble of Trees (GENIE3) - 215:20
Discussion15:59
Experiments within the DREAM challenges17:47
Assessment of GRN inference techniques - 118:21
Assessment of GRN inference techniques - 219:50
The DREAM challenges21:09
Experiments within the DREAM challenges22:23
Steady-state data and microarray compendia - 122:30
Steady-state data and microarray compendia - 222:31
DREAM5 Network Inference Challenge22:46
Evaluation protocol (all challenges)23:52
Comparison with other methods - 124:21
Comparison with other methods - 224:23
Comparison with other methods - 327:41
The Wisdom of crowds28:26
Validation of the community prediction: E. coli29:55
Time-series (and steady-state) data - 130:36
Time-series (and steady-state) data - 230:45
GENIE3 with time-series data31:07
DREAM3 and DREAM4 In Silico Size10033:45
Results34:24
Wisdom of crowds for time series35:31
Genotype data36:17
Expression + Genotype data36:26
Systems genetics36:35
GENIE3 with systems genetics data37:49
GENIE3-SG-joint42:07
GENIE3-SG-sep42:08
DREAM5 Systems Genetics Challenge42:09
GENIE3 with systems genetics data42:55
GENIE3-SG-sep outperforms DREAM5 best performers44:23
Conclusions45:30
Future works47:39
References50:20