<i>AB INITIO</i> HUMAN miRNA AND PRE-miRNA PREDICTION

Titov, I. I.; Vorozheykin, P. S.

doi:10.1142/s0219720013430099

Cited by 3 publications

(4 citation statements)

References 42 publications

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“…The results shown in Tables 3 and 4 for TVM were taken from its publication [23], while the results for miRNAFold, miRPara, CID-miRNA, and VMir were obtained from the work of Tempel and Tahi [14]. Using the same criterion as the authors of miRNAFold, a predicted pre-miRNA is considered true if the distance from its center to the center of the known hairpin is less or equal to 10% of the size of the latter.…”

Section: Resultsmentioning

confidence: 99%

“…The method of Titov and Vorozheykin (TVM) proposes a context-structural Markov model and a hidden Markov model for ab initio prediction of pre-miRNAs and miRNAs, respectively [23]. The authors used 1,872 human pre-miRNAs from the miRBase database as positive instances, and 1,872 random sequences from the pseudo-precursors constructed by Xue and colleagues as negative instances [24].…”

Section: Introductionmentioning

confidence: 99%

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Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

et al. 2016

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BackgroundMicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets.ResultsBy comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools.ConclusionsThe extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

et al. 2016

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“…Computational techniques for the de novo prediction of pre-miRNA sequences within larger genomic sequences—referred to as ‘miRNA prediction’ within this text—can be broadly separated into two categories: homology-based prediction ( 14 – 18 ) and machine-learning-based prediction ( 19 – 42 ). Homology-based methods predict miRNA based on similarity to other known miRNA, with respect to sequence, structure or target site.…”

Section: Introductionmentioning

confidence: 99%

“…The largest class of de novo miRNA prediction tools are machine-learning-based classifiers which separate true miRNA from miRNA-like structures, based on elements of primary sequence and secondary structure. A wide array of classification techniques have been applied to this problem, including random forests ( 35 , 37 ), hidden Markov models ( 22 , 42 ), naive Bayes classifiers ( 34 ) and KNN classifiers ( 31 ). The most common technique is support vector machines (SVMs) ( 32 , 38 – 39 , 41 ).…”

Section: Introductionmentioning

confidence: 99%

A framework for improving microRNA prediction in non-human genomes

et al. 2015

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The prediction of novel pre-microRNA (miRNA) from genomic sequence has received considerable attention recently. However, the majority of studies have focused on the human genome. Previous studies have demonstrated that sensitivity (correctly detecting true miRNA) is sustained when human-trained methods are applied to other species, however they have failed to report the dramatic drop in specificity (the ability to correctly reject non-miRNA sequences) in non-human genomes. Considering the ratio of true miRNA sequences to pseudo-miRNA sequences is on the order of 1:1000, such low specificity prevents the application of most existing tools to non-human genomes, as the number of false positives overwhelms the true predictions. We here introduce a framework (SMIRP) for creating species-specific miRNA prediction systems, leveraging sequence conservation and phylogenetic distance information. Substantial improvements in specificity and precision are obtained for four non-human test species when our framework is applied to three different prediction systems representing two types of classifiers (support vector machine and Random Forest), based on three different feature sets, with both human-specific and taxon-wide training data. The SMIRP framework is potentially applicable to all miRNA prediction systems and we expect substantial improvement in precision and specificity, while sustaining sensitivity, independent of the machine learning technique chosen.

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Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

Haque

et al. 2020

Interdiscip Sci Comput Life Sci

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AB INITIO HUMAN miRNA AND PRE-miRNA PREDICTION

Cited by 3 publications

References 42 publications

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

A framework for improving microRNA prediction in non-human genomes

Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

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