title: A knowledge driven supervised learning approach to identify gene network of differentially up-regulated genes during neuronal senescence in Rattus norvegicus.
abstract: Various approaches have been described to infer the gene interaction network from expression data. Several models based on computational and mathematical methods are available. The fundamental thing in the identification of the gene interaction is their biological relevance. Two genes belonging to the same pathway are more likely to affect the expression of each other than the genes of two different pathways. In the present study, interaction network of genes is described based on upregulated genes during neuronal senescence in the Cerebellar granule neurons of rat. We have adopted a supervised learning method and used it in combination with biological pathway information of the genes to develop a gene interaction network. Further modular analysis of the network has been done to identify senescence-related marker genes. Currently there is no adequate information available about the genes implicated in neuronal senescence. Thus identifying multipath genes belonging to the pathway affected by senescence might be very useful in studying the senescence process.
authors.affiliation: Department of Biotechnology and Bioinfomatics, School of Life Sciences, University of Hyderabad, Hyderabad 500046, Telangana, India; Cognitive Science Lab, International Institute of Information Technology (IIIT) Hyderabad, Hyderabad 500032, Telangana, India.
authors.forename: Pankaj Singh
authors.initials: PS
authors.lastname: Dholaniya
authors.affiliation: School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, Telangana, India; Cognitive Science Lab, International Institute of Information Technology (IIIT) Hyderabad, Hyderabad 500032, Telangana, India.
authors.forename: Soumitra
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, Telangana, India; Cognitive Science Lab, International Institute of Information Technology (IIIT) Hyderabad, Hyderabad 500032, Telangana, India.
authors.forename: Bapi Raju
authors.initials: BR
authors.lastname: Surampudi
authors.affiliation: Department of Biotechnology and Bioinfomatics, School of Life Sciences, University of Hyderabad, Hyderabad 500046, Telangana, India; Cognitive Science Lab, International Institute of Information Technology (IIIT) Hyderabad, Hyderabad 500032, Telangana, India. Electronic address: akondapi@gmail.com.
authors.forename: Anand K
authors.initials: AK
authors.lastname: Kondapi
mesh_terms: D000818:Animals; D016922:Cellular Senescence / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D003663:Decision Trees; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D000069550:Machine Learning; D009474:Neurons / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis; D051381:Rats; D015854:Up-Regulation
keywords: Aging; Gene network; Senescence; Supervised Learning