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RNA-seq data analysis using nonparametric Gaussian process models

Nguyen, Thanh Thi, Nahavandi, Saeid, Creighton, Douglas and Khosravi, Abbas 2016, RNA-seq data analysis using nonparametric Gaussian process models, in Proceedings of the International Joint Conference on Neural Networks, pp. 5087-5093, doi: 10.1109/IJCNN.2016.7727870.


Title RNA-seq data analysis using nonparametric Gaussian process models
Author(s) Nguyen, Thanh Thi
Nahavandi, Saeid
Creighton, Douglas
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Title of proceedings Proceedings of the International Joint Conference on Neural Networks
Publication date 2016-10-31
Start page 5087
End page 5093
Total pages 7
Summary © 2016 IEEE.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.
ISBN 9781509006199
DOI 10.1109/IJCNN.2016.7727870
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092213

Document type: Conference Paper
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