Name
Abstract | ||
|---|---|---|
Reverse engineering whole-genome networks from large-scale gene expression measurements and analyzing them to extract biologically valid hypotheses are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. In this talk, I will present our research on the development of parallel mutual information and Bayesian network based structure learning methods to eliminate such bottlenecks and facilitate genome-scale network inference. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer. Such networks can be used as a guide to predicting gene function and extracting context-specific subnetworks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BIBM.2015.7359646 | IEEE International Conference on Bioinformatics and Biomedicine |
Keywords | Field | DocType |
context-specific subnetworks,gene function,Tianhe-2 supercomputer,microarray experiments,model plant Arabidopsis thaliana,genome-scale network inference,Bayesian network based structure learning methods,parallel mutual information,intensive limiting,gene expression datasets,system biology,large-scale gene expression measurements,reverse engineering genome-scale networks,parallel machine learning approaches | Genome,Data mining,Computer science,Artificial intelligence,Supercomputer,Inference,Reverse engineering,Systems biology,Bayesian network,Mutual information,Bioinformatics,Limiting,Machine learning | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aluru, Srinivas | 1 | 1166 | 122.83 |