Handling class imbalance problem in miRNA dataset associated with cancer

Bioinformation
Ram Kothandan

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 i...Continue Reading

Citations

Apr 12, 2019·Reproduction in Domestic Animals = Zuchthygiene·Zhi ChenZhangping Yang

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Methods Mentioned

BETA
feature extraction

Software Mentioned

miRINT
WEKA
Pairfinder
MiRSEQ
LIBSVM
Waikato Environment for Knowledge Analysis ( WEKA )
TargetMiner
SMOTE

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