Recently, some non-coding small RNAs, known as microRNAs (miRNA), have drawn a lot of attention to identify their role in gene regulation and various biological processes. The miRNA profiles are surprisingly informative, reflecting the malignancy state of the tissues. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. Here we use the expression profile of 217 miRNAs from 186 samples, including multiple human cancers. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, support vector machine, and k-nearest neighbor have been used for classification. Experimental results indicate that k-nearest neighbor with cosine coefficient produces the best result, 95.0% of recognition rate on the test data.
|Title of host publication||Neural Information Processing|
|Subtitle of host publication||Models and Applications - 17th International Conference, ICONIP 2010, Proceedings|
|Number of pages||8|
|Publication status||Published - 2010|
|Event||17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia|
Duration: 2010 Nov 22 → 2010 Nov 25
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||17th International Conference on Neural Information Processing, ICONIP 2010|
|Period||10/11/22 → 10/11/25|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0012876) (2010-0018948).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)