Supatcha Lertampaiporn

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An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification(More)
To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of(More)
This article presents a new viral precursor miRNAs identification tool using back-propagation neural network. The tool mainly discriminates the viral precursor miRNAs from coding sequences and other pseudo precursor miRNAs. It was trained with viral precursor miRNAs from miRBase, pseudo precursor miRNAs and coding sequences from ViralmiR and NCBI database,(More)
This work presents an identification tool for plant precursor miRNAs (pre-miRNAs) using structural robustness and derivative features which can improve performance in discriminating the plant pre-miRNAs from pseudo pre-miRNAs. The classification models were trained with plant pre-miRNAs and pseudo hairpins datasets from PlantMiRNAPred web site. The top 20(More)
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