RNA-seq data analysis using nonparametric Gaussian process models

Thanh Nguyen, Saeid Nahavandi, Douglas Creighton, Abbas Khosravi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

This paper introduces an approach to classification of RNA-seq read count data using Gaussian process (GP) models. RNA-seq data are transformed into microarray-like data before applying the statistical two-sample t-test for gene selection. GP is designed as a classifier that takes discriminant genes selected by the t-test method as inputs. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation strategy. Various performance metrics that include accuracy rate, F-measure, area under the ROC curve and mutual information are used to evaluate the classifiers. Experimental results show the significant dominance of the GP classifier against its competing methods including k-nearest neighbors, multilayer perceptron, support vector machine and ensemble learning AdaBoost. The proposed approach therefore can be implemented effectively in real practice for RNA-seq data analysis, which is useful in many applications related to disease diagnosis and monitoring at the molecular level.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages5087-5093
Number of pages7
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
https://ewh.ieee.org/conf/wcci/2016/
https://ieeexplore.ieee.org/xpl/conhome/7593175/proceeding (Proceedings)

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2016
Abbreviated titleIJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16
Internet address

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