title: Learning biological network using mutual information and conditional independence.
abstract: BACKGROUND
  Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses.
  
  
  RESULTS
  To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance of the proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases.
  
  
  CONCLUSIONS
  By using a new protein microarray technology in combination with a new computational framework, we demonstrate an application of the methodology to the study of biological networks of ATM cell lines under low dose ionization radiation.
authors.affiliation: Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.
authors.forename: Dong-Chul
authors.initials: DC
authors.lastname: Kim
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Wang
authors.forename: Chin-Rang
authors.initials: CR
authors.lastname: Yang
authors.forename: Jean
authors.initials: J
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D064007:Ataxia Telangiectasia Mutated Proteins; D001499:Bayes Theorem; D018797:Cell Cycle Proteins / Q000235:genetics / Q000378:metabolism; D002460:Cell Line; D004268:DNA-Binding Proteins / Q000235:genetics / Q000378:metabolism; D004307:Dose-Response Relationship, Radiation; D053263:Gene Regulatory Networks; D040081:Protein Array Analysis / Q000379:methods*; D017346:Protein Serine-Threonine Kinases / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D045663:Quantum Dots; D015203:Reproducibility of Results; D015398:Signal Transduction; D025521:Tumor Suppressor Proteins / Q000235:genetics / Q000378:metabolism
chemical_list: D018797:Cell Cycle Proteins; D004268:DNA-Binding Proteins; D025521:Tumor Suppressor Proteins; D064007:Ataxia Telangiectasia Mutated Proteins; D017346:Protein Serine-Threonine Kinases