Literature DB >> 22408193

Defining and providing robust controls for microRNA prediction.

William Ritchie1, Dadi Gao, John E J Rasko.   

Abstract

MOTIVATION: microRNAs are short non-coding RNAs that regulate gene expression by inhibiting target mRNA genes. Next-generation sequencing combined with bioinformatics analyses provide an opportunity to predict numerous novel miRNAs. The efficiency of these predictions relies on the set of positive and negative controls used. We demonstrate that commonly used positive and negative controls may be unreliable and provide a rational methodology with which to replace them.

Mesh:

Substances:

Year:  2012        PMID: 22408193     DOI: 10.1093/bioinformatics/bts114

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  14 in total

1.  miREval 2.0: a web tool for simple microRNA prediction in genome sequences.

Authors:  Dadi Gao; Robert Middleton; John E J Rasko; William Ritchie
Journal:  Bioinformatics       Date:  2013-09-18       Impact factor: 6.937

2.  Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification.

Authors:  Supatcha Lertampaiporn; Chinae Thammarongtham; Chakarida Nukoolkit; Boonserm Kaewkamnerdpong; Marasri Ruengjitchatchawalya
Journal:  Nucleic Acids Res       Date:  2012-09-24       Impact factor: 16.971

3.  Fast selection of miRNA candidates based on large-scale pre-computed MFE sets of randomized sequences.

Authors:  Sven Warris; Sander Boymans; Iwe Muiser; Michiel Noback; Wim Krijnen; Jan-Peter Nap
Journal:  BMC Res Notes       Date:  2014-01-13

4.  mirMark: a site-level and UTR-level classifier for miRNA target prediction.

Authors:  Mark Menor; Travers Ching; Xun Zhu; David Garmire; Lana X Garmire
Journal:  Genome Biol       Date:  2014       Impact factor: 13.583

5.  Computational methods for ab initio detection of microRNAs.

Authors:  Jens Allmer; Malik Yousef
Journal:  Front Genet       Date:  2012-10-10       Impact factor: 4.599

6.  Evidence for the biogenesis of more than 1,000 novel human microRNAs.

Authors:  Marc R Friedländer; Esther Lizano; Anna J S Houben; Daniela Bezdan; Mónica Báñez-Coronel; Grzegorz Kudla; Elisabet Mateu-Huertas; Birgit Kagerbauer; Justo González; Kevin C Chen; Emily M LeProust; Eulàlia Martí; Xavier Estivill
Journal:  Genome Biol       Date:  2014-04-07       Impact factor: 13.583

7.  The discriminant power of RNA features for pre-miRNA recognition.

Authors:  Ivani de O N Lopes; Alexander Schliep; André C P de L F de Carvalho
Journal:  BMC Bioinformatics       Date:  2014-05-02       Impact factor: 3.169

8.  Global population-specific variation in miRNA associated with cancer risk and clinical biomarkers.

Authors:  Renata A Rawlings-Goss; Michael C Campbell; Sarah A Tishkoff
Journal:  BMC Med Genomics       Date:  2014-08-28       Impact factor: 3.063

9.  Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

Authors:  Malik Yousef; Müşerref Duygu Saçar Demirci; Waleed Khalifa; Jens Allmer
Journal:  Adv Bioinformatics       Date:  2016-04-12

10.  The impact of feature selection on one and two-class classification performance for plant microRNAs.

Authors:  Malik Yousef; Jens Allmer; Waleed Khalifa; Müşerref Duygu Saçar Demirci
Journal:  PeerJ       Date:  2016-06-21       Impact factor: 2.984

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