title: Complex network-based approaches to biomarker discovery.
abstract: Many studies on biomarker discovery have been done by analyzing mutations in DNA sequences and differences in gene expression patterns. As a new branch of the latter approach, the concept of network biomarkers has been proposed, in which expression data of small subnetworks are used as markers. Furthermore, network biomarkers have been extended to dynamical network biomarkers, in which time series expression data of subnetworks are used as markers. On the other hand, the methodologies in complex networks have also been applied to biomarker discovery. For example, various centrality measures and the concept of observability have been applied. In this article, we review these new approaches for biomarker discovery with focusing on the computational/methodological aspects.
authors.affiliation: Kyoto University, Gokasho, Uji, Kyoto, Japan 611-0011.
authors.forename: Morihiro
authors.initials: M
authors.lastname: Hayashida
authors.affiliation: Kyoto University, Gokasho, Uji, Kyoto, Japan 611-0011.
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D060066:Protein Interaction Maps; D046888:Tissue Array Analysis
chemical_list: D015415:Biomarkers
keywords: centrality; network biomarkers; observability