title: A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.
abstract: MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria.
  
  
  
  This study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster members are assessed as potential biomarkers. Both the criteria for the partition of clusters and selection of representative members are based on Fisher linear discriminant analysis (FDA). The FDA-based criterion has been demonstrated to be superior to three other criteria in selecting representative members, and also good at refining clusters. In the comparison with eight common feature selection methods, this clustering-based method performs the best with regard to the discriminative ability of selected biomarkers.
  
  
  
  Our experimental results demonstrate that the clustering-based method can identify microRNA combinatorial biomarkers with high accuracy and efficiency. Our method and data are available to the public upon request.
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240, Shanghai, China. yangyang@cs.sjtu.edu.cn.|Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China. yangyang@cs.sjtu.edu.cn.|Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai, China. yangyang@cs.sjtu.edu.cn.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240, Shanghai, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Huang
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240, Shanghai, China.
authors.forename: Luning
authors.initials: L
authors.lastname: Hao
authors.affiliation: Department of Computer Science and Engineering, Shanghai Maritime University, 1550 Hai Gang Ave., Shanghai, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D016000:Cluster Analysis; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs
keywords: Biomarker; Clustering; MicroRNA