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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
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
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Document type: Conference Paper
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