title: PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.
abstract: Technology advances have immensely accelerated large-scale mapping of biological networks, which necessitates the development of accurate and powerful network-based algorithms to make functional inferences. A prevailing approach is to leverage functions of neighboring nodes to predict unknown molecular function. However, existing neighbor-based algorithms have ignored the scale-free property hidden in many biological networks. By assuming that neighbor sharing is constrained by the preferential attachment property, we developed a Preferential Attachment based common Neighbor Distribution (PAND) to calculate the probability of the neighbor-sharing event between any two nodes in scale-free networks, which nearly perfectly matched the observed probability in simulations. By applying PAND to a human protein-protein interaction (PPI) network, we showed that smaller probabilities represented closer functional linkages between proteins. With the PAND-derive linkages, we were able to build new networks where the links are more functionally reliable than those of the human PPI network. We then applied simple annotation schemes to a PAND-derived network to make reliable functional predictions for proteins. We also developed an R package called PANDA (PAND-derived functional Associations) to implement the methods proposed in this study. In conclusion, PAND is a useful distribution to calculate the probability of the neighbor-sharing events in scale-free networks. With PAND, we are able to extract reliable functional linkages from real biological networks and builds new networks that are better bases for further functional inference.
authors.affiliation: Bio-ID Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, United States of America.
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, United States of America.
authors.forename: Pan
authors.initials: P
authors.lastname: Tong
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, 77030, United States of America.
authors.forename: Juan
authors.initials: J
authors.lastname: Gallegos
authors.affiliation: The EMBL Outstation-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.
authors.forename: Emily
authors.initials: E
authors.lastname: Dimmer
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, United States of America.
authors.forename: Guoshuai
authors.initials: G
authors.lastname: Cai
authors.affiliation: Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, United States of America.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Molldrem
authors.affiliation: Bio-ID Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, United States of America.
authors.forename: Shoudan
authors.initials: S
authors.lastname: Liang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D060066:Protein Interaction Maps