BACKGROUND: Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. RESULTS: We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. CONCLUSIONS: We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks.
BACKGROUND: Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. RESULTS: We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. CONCLUSIONS: We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks.
Authors: Kwang-Il Goh; Michael E Cusick; David Valle; Barton Childs; Marc Vidal; Albert-László Barabási Journal: Proc Natl Acad Sci U S A Date: 2007-05-14 Impact factor: 11.205
Authors: Michael Costanzo; Anastasia Baryshnikova; Jeremy Bellay; Yungil Kim; Eric D Spear; Carolyn S Sevier; Huiming Ding; Judice L Y Koh; Kiana Toufighi; Sara Mostafavi; Jeany Prinz; Robert P St Onge; Benjamin VanderSluis; Taras Makhnevych; Franco J Vizeacoumar; Solmaz Alizadeh; Sondra Bahr; Renee L Brost; Yiqun Chen; Murat Cokol; Raamesh Deshpande; Zhijian Li; Zhen-Yuan Lin; Wendy Liang; Michaela Marback; Jadine Paw; Bryan-Joseph San Luis; Ermira Shuteriqi; Amy Hin Yan Tong; Nydia van Dyk; Iain M Wallace; Joseph A Whitney; Matthew T Weirauch; Guoqing Zhong; Hongwei Zhu; Walid A Houry; Michael Brudno; Sasan Ragibizadeh; Balázs Papp; Csaba Pál; Frederick P Roth; Guri Giaever; Corey Nislow; Olga G Troyanskaya; Howard Bussey; Gary D Bader; Anne-Claude Gingras; Quaid D Morris; Philip M Kim; Chris A Kaiser; Chad L Myers; Brenda J Andrews; Charles Boone Journal: Science Date: 2010-01-22 Impact factor: 47.728
Authors: Stacia R Engel; Rama Balakrishnan; Gail Binkley; Karen R Christie; Maria C Costanzo; Selina S Dwight; Dianna G Fisk; Jodi E Hirschman; Benjamin C Hitz; Eurie L Hong; Cynthia J Krieger; Michael S Livstone; Stuart R Miyasato; Robert Nash; Rose Oughtred; Julie Park; Marek S Skrzypek; Shuai Weng; Edith D Wong; Kara Dolinski; David Botstein; J Michael Cherry Journal: Nucleic Acids Res Date: 2009-11-11 Impact factor: 16.971