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MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.
A comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.
A 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.
This article was published in the following journal.
Name: BMC bioinformatics
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Predicting the time of OVULATION can be achieved by measuring the preovulatory elevation of ESTRADIOL; LUTEINIZING HORMONE or other hormones in BLOOD or URINE. Accuracy of ovulation prediction depends on the completeness of the hormone profiles, and the ability to determine the preovulatory LH peak.
Small double-stranded, non-protein coding RNAs, 21-25 nucleotides in length generated from single-stranded microRNA gene transcripts by the same RIBONUCLEASE III, Dicer, that produces small interfering RNAs (RNA, SMALL INTERFERING). They become part of the RNA-INDUCED SILENCING COMPLEX and repress the translation (TRANSLATION, GENETIC) of target RNA by binding to homologous 3'UTR region as an imperfect match. The small temporal RNAs (stRNAs), let-7 and lin-4, from C. elegans, are the first 2 miRNAs discovered, and are from a class of miRNAs involved in developmental timing.
Performance of an act one or more times, with a view to its fixation or improvement; any performance of an act or behavior that leads to learning.
Planning that has the goals of improving health, improving accessibility to health services, and promoting efficiency in the provision of services and resources on a comprehensive basis for a whole community. (From Facts on File Dictionary of Health Care Management, 1988, p299)
Carrying out of specific physical routines or procedures by one who is trained or skilled in physical activity. Performance is influenced by a combination of physiological, psychological, and socio-cultural factors.
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Bioinformatics is the application of computer software and hardware to the management of biological data to create useful information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied...
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