title: Evaluating predictive performance of network biomarkers with network structures.
abstract: Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China , Department of Computer Science, University of Calgary, 2500 University Drive N. W., Calgary, Alberta, Canada.
authors.forename: Shang
authors.initials: S
authors.lastname: Gao
authors.forename: Ibrahim
authors.initials: I
authors.lastname: Karakira
authors.forename: Salim
authors.initials: S
authors.lastname: Afra
authors.forename: Ghada
authors.initials: G
authors.lastname: Naji
authors.forename: Reda
authors.initials: R
authors.lastname: Alhajj
authors.forename: Jia
authors.initials: J
authors.lastname: Zeng
authors.forename: Douglas
authors.initials: D
authors.lastname: Demetrick
mesh_terms: D000465:Algorithms; D015415:Biomarkers; D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D011237:Predictive Value of Tests
chemical_list: D015415:Biomarkers; D014408:Biomarkers, Tumor; D005819:Genetic Markers
keywords: Network model; breast cancer; graph Laplacian; network markers; optimization