title: Classification of time series gene expression in clinical studies via integration of biological network.
abstract: The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.
authors.affiliation: School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China.
authors.forename: Liwei
authors.initials: L
authors.lastname: Qian
authors.forename: Haoran
authors.initials: H
authors.lastname: Zheng
authors.forename: Hong
authors.initials: H
authors.lastname: Zhou
authors.forename: Ruibin
authors.initials: R
authors.lastname: Qin
authors.forename: Jinlong
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D016899:Interferon-beta / Q000627:therapeutic use; D008954:Models, Biological; D009103:Multiple Sclerosis / Q000188:drug therapy / Q000235:genetics; D060066:Protein Interaction Maps; D015203:Reproducibility of Results; D016896:Treatment Outcome
chemical_list: D016899:Interferon-beta