Handling class imbalance problem in miRNA dataset associated with cancer |
| |
Authors: | Ram Kothandan |
| |
Institution: | Department of Biological Sciences, BITS PILANI K K Birla Goa Campus, Zuarinagar, Vasco Da Gama, India |
| |
Abstract: | MiRNAs are small (~22nt long) non-coding RNA sequences; binds to the complementarity target sites in 3'' Untranslated Region
(UTR) of mRNA sequences but not restricted to other mRNA regions viz., 5'' UTR and Coding sequences (CDS). Complementarity
binding of miRNA to mRNA target sites either results in complete degradation of the mRNA itself or it may regulate the mRNA as
an oncogene or as a tumor suppressor gene. However, the exact mechanism involved in identifying a miRNA to be associated with
cancer is still unclear. Further, with the outburst in the number of miRNAs sequences recorded every year in miRBase, the gap is
still widening mainly due to the laborious and economically unfavorable experimental procedures associated with the functional
annotation. Motivated by the fact, we constructed a two-step support vector machine-based predictive model - miRSEQ and
miRINT. However, the major pitfall during the construction of the model is the class imbalance problem. Hence, in order to
overcome class imbalance problem, in the present study we empirically compare the effectiveness of two different methods viz.,
Synthetic Minority Oversampling Technique (SMOTE) and cost-senstive learning method. Performance measures were evaluated
in terms of Precision and Recall. Based on our result, it was observed that for miRNA dataset with high class imbalance utilized for
predicting association of cancer, cost-sensitive method outperformed the oversampling method. |
| |
Keywords: | Cost-sensitive SMOTE miRNA-mRNA interaction Support Vector Machines |
|
|