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 language | English |
---|---|
Title of host publication | 2016 International Joint Conference on Neural Networks, IJCNN 2016 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 5087-5093 |
Number of pages | 7 |
ISBN (Electronic) | 9781509006199 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | IEEE International Joint Conference on Neural Networks 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 https://ewh.ieee.org/conf/wcci/2016/ https://ieeexplore.ieee.org/xpl/conhome/7593175/proceeding (Proceedings) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2016 |
---|---|
Abbreviated title | IJCNN 2016 |
Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Internet address |