Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set

Abstract

MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing data sets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative. To generate a representative negative set, we proposed a novel negative sample selection technique, and successfully collected negative samples with improved quality. Two recent classifiers rebuilt with the proposed negative set achieved an improvement of ~6 percent in their predictive performance, which confirmed this assumption. Based on the proposed negative set, we constructed a training set, and developed an online system called miRNApre specifically for human pre-miRNA identification. We showed that miRNApre achieved accuracies on updated human and non-human data sets that were 34.3 and 7.6 percent higher than those achieved by current methods. The results suggest that miRNApre is an effective tool for pre-miRNA identification. Additionally, by integrating miRNApre, we developed a miRNA mining tool, mirnaDetect, which can be applied to find potential miRNAs in genome-scale data. MirnaDetect achieved a comparable mining performance on human chromosome 19 data as other existing methods.

iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

“…Features derived from RNA sequences or their predicted secondary structures were proposed to capture the characteristics of miRNAs, for example, k-mer (sub-sequence of RNAs) is one of the main sequence-based features reflecting the local sequence composition of RNAs (Wei et al, 2014). Because most of the pre-miRNAs have the characteristic of stemloop hairpin structures (Xue et al, 2005), some features were constructed based on the predicted secondary structures so as to reflect this characteristic; e.g., a set of 32 local triplet sequence-structure features were used in the Triplet-SVM (Xue et al, 2005) to predict the human miRNAs (Xue et al, 2005).…”

iEnhancer-PsedeKNC: Identification of enhancers and their subgroups based on Pseudo degenerate kmer nucleotide composition

“…In our future work, we will try other features considering long range sequence information, which is indicted to be useful for the enhancer classification in our current work. We will also try the imbalanced classifiers [52][53][54][55][56]on our dataset, which has been employed CD-HIT and random sampling strategy for the large negative data. Combined with some more sophisticated machine learning models and feature reduction methods [57], we anticipate better performance can be achieved.…”

Computational identification of miRNAs and their targets from Niger (Guizotia abyssinica)

“…mirnaDetect (http://datamining.xmu.edu.cn/main/~leyiwei/mirnaDetect.html) a tool for detecting potential pre-miRNAs from the genome-scale data was also used to predict the possible miRNAs in Niger [15]. The secondary structures of putative pre-miRNAs were predicted by Mfold (http://mfold.rna.albany.edu) [16].…”