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Gene regulatory network inference using tree-based ensemble methods
Published on 2013-07-093715 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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Presentation
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
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-SG-sep outperforms DREAM5 best performers44:23
Conclusions45:30
Future works47:39
References50:20