title: Minimum dominating set-based methods for analyzing biological networks.
abstract: The fast increase of 'multi-omics' data does not only pose a computational challenge for its analysis but also requires novel algorithmic methodologies to identify complex biological patterns and decipher the ultimate roots of human disorders. To that end, the massive integration of omics data with disease phenotypes is offering a new window into the cell functionality. The minimum dominating set (MDS) approach has rapidly emerged as a promising algorithmic method to analyze complex biological networks integrated with human disorders, which can be composed of a variety of omics data, from proteomics and transcriptomics to metabolomics. Here we review the main theoretical foundations of the methodology and the key algorithms, and examine the recent applications in which biological systems are analyzed by using the MDS approach.
authors.affiliation: Department of Information Science, Faculty of Science, Toho University, Miyama 2-2-1, Funabashi, Chiba 274-8510, Japan. Electronic address: nacher@is.sci.toho-u.ac.jp.
authors.forename: Jose C
authors.initials: JC
authors.lastname: Nacher
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. Electronic address: takutsu@kuicr.kyoto-u.ac.jp.
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D066264:Datasets as Topic; D006801:Humans; D055432:Metabolomics; D010641:Phenotype; D060066:Protein Interaction Maps; D040901:Proteomics
keywords: Complex networks; Minimum dominating set; Network controllability; Protein-protein interaction networks