title: Optimization of Gene Set Annotations Using Robust Trace-Norm Multitask Learning.
abstract: Gene set enrichment (GSE) is a useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets annotations. In this paper, we propose a novel method, robust trace-norm multitask learning, to solve the optimization problem of gene set annotations. Inspired by the binary nature of annotations, we convert the optimization of gene set annotations into a weakly supervised classification problem and use discriminative logistic regression to fit these datasets. Then, the output of logistic regression can be used to measure the probability of the existence of annotations. In addition, the optimization of each row of the annotation matrix can be treated as an independent weakly classification task, and we use the multitask learning approach with trace-norm regularization to optimize all rows of annotation matrix simultaneously. Finally, the experiments on simulated and real data demonstrate the effectiveness and good performance of the proposed method.
authors.forename: Xianpeng
authors.initials: X
authors.lastname: Liang
authors.forename: Lin
authors.initials: L
authors.lastname: Zhu
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D000069553:Supervised Machine Learning