Bhadra, Sahely and Bhattacharyya, Chiranjib and Chandra, Nagasuma R and Mian, I Saira (2009) A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data. In: Algorithms for Molecular Biology, 4 (5). pp. 115.

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Abstract
Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from highdimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the LeaveOneOut Error. The accuracy and utility of LPSLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LPSLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LPSLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LPSLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in realworld networks. Inspection of these LPSLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LPbased method for estimating the topology of a large sparse undirected graph from highdimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LPSLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LPbased solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.
Item Type:  Journal Article 

Additional Information:  Copyright of this article belongs to BioMed Central. 
Department/Centre:  Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation) 
Date Deposited:  28 Aug 2009 15:21 
Last Modified:  19 Sep 2010 05:30 
URI:  http://eprints.iisc.ernet.in/id/eprint/19848 
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