BicNET: Flexible module discovery in large-scale biological networks using biclustering. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Henriques, Rui; Madeira, Sara
• Is Part Of: Algorithms for molecular biology. Volume 11:Issue 1(2016)
• Page Start: 1
• Page End: 30
• Publication Date: 2016-12
• Subjects: Flexible module discovery -- Large-scale biological networks -- Biclustering; Molecular biology -- Mathematical models -- Periodicals; Algorithms -- Periodicals; Bioinformatics -- Periodicals; 572.8015118
• DOI: 10.1186/s13015-016-0074-8
• Languages: English
• ISSNs: 1748-7188

Abstract

Abstract Background Despite the recognized importance of module discovery in biological networks to enhance our understanding of complex biological systems, existing methods generally suffer from two major drawbacks. First, there is a focus on modules where biological entities are strongly connected, leading to the discovery of trivial/well-known modules and to the inaccurate exclusion of biological entities with subtler yet relevant roles. Second, there is a generalized intolerance towards different forms of noise, including uncertainty associated with less-studied biological entities (in the context of literature-driven networks) and experimental noise (in the context of data-driven networks). Although state-of-the-art biclustering algorithms are able to discover modules with varying coherency and robustness to noise, their application for the discovery of non-dense modules in biological networks has been poorly explored and it is further challenged by efficiency bottlenecks. Methods This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. Three major contributions are provided. First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. Second, we propose an algorithm to discover these modules and to robustly handle noisy and missing interactions. Finally, we provide new searches to tackle time and memory bottlenecks by effectively exploring the inherent structural sparsity of network data. Results Results in synthetic network data confirm the soundness, efficiency and superiority of BicNET. The application of BicNET on protein interaction and gene interaction networks from yeast, E. coli and Human reveals new modules with heightened biological significance. Conclusions BicNET is, to our knowledge, the first method enabling the efficient unsupervised analysis of large-scale network data for the discovery of coherent modules with parameterizable homogeneity.