title: Link clustering reveals structural characteristics and biological contexts in signed molecular networks.
abstract: Many biological networks are signed molecular networks which consist of positive and negative links. To reveal the distinct features between links with different signs, we proposed signed link-clustering coefficients that assess the similarity of inter-action profiles between linked molecules. We found that positive links tended to cluster together, while negative links usually behaved like bridges between positive clusters. Positive links with higher adhesiveness tended to share protein domains, be associated with protein-protein interactions and make intra-connections within protein complexes. Negative links that were more bridge-like tended to make interconnections between protein complexes. Utilizing the proposed measures to group positive links, we observed hierarchical modules that could be well characterized by functional annotations or known protein complexes. Our results imply that the proposed sign-specific measures can help reveal the network structural characteristics and the embedded biological contexts of signed links, as well as the functional organization of signed molecular networks.
authors.affiliation: Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Chen-Ching
authors.initials: CC
authors.lastname: Lin
authors.forename: Chia-Hsien
authors.initials: CH
authors.lastname: Lee
authors.forename: Chiou-Shann
authors.initials: CS
authors.lastname: Fuh
authors.forename: Hsueh-Fen
authors.initials: HF
authors.lastname: Juan
authors.forename: Hsuan-Cheng
authors.initials: HC
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics*