title: Combining gene expression and interaction network data to improve kidney lesion score prediction.
abstract: Current method of diagnosing kidney rejection based on histopathology of renal biopsies in form of lesion scores is error-prone. Researchers use gene expression microarrays in combination of machine learning to build better kidney rejection predictors. However the high dimensionality of data makes this task challenging and compels application of feature selection methods. We present a method for predicting lesions using combination of statistical and biological feature selection methods along with an ensemble learning technique. Results show that combining highly interacting genes (Hub Genes) from protein-protein interaction network with genes selected by squared t-test method brings the most accurate kidney lesion score predictor.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. moulavi@ualberta.ca
authors.forename: Davoud
authors.initials: D
authors.lastname: Moulavi
authors.forename: Mohsen
authors.initials: M
authors.lastname: Hajiloo
authors.forename: Jorg
authors.initials: J
authors.lastname: Sander
authors.forename: Philip F
authors.initials: PF
authors.lastname: Halloran
authors.forename: Russell
authors.initials: R
authors.lastname: Greiner
mesh_terms: D001185:Artificial Intelligence; D016208:Databases, Factual; D015870:Gene Expression; D020869:Gene Expression Profiling; D006084:Graft Rejection / Q000378:metabolism* / Q000473:pathology; D006801:Humans; D007668:Kidney / Q000378:metabolism* / Q000473:pathology; D016030:Kidney Transplantation; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine