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Bioinformatics in MicroRNA Research

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Cover of 'Bioinformatics in MicroRNA Research'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 MicroRNAs, Long Noncoding RNAs, and Their Functions in Human Disease
  3. Altmetric Badge
    Chapter 2 MicroRNA Expression: Protein Participants in MicroRNA Regulation
  4. Altmetric Badge
    Chapter 3 Viral MicroRNAs, Host MicroRNAs Regulating Viruses, and Bacterial MicroRNA-Like RNAs
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    Chapter 4 MicroRNAs: Biomarkers, Diagnostics, and Therapeutics
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    Chapter 5 Relational Databases and Biomedical Big Data
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    Chapter 6 Semantic Technologies and Bio-Ontologies
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    Chapter 7 Genome-Wide Analysis of MicroRNA-Regulated Transcripts
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    Chapter 8 Computational Prediction of MicroRNA Target Genes, Target Prediction Databases, and Web Resources
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    Chapter 9 Exploring MicroRNA::Target Regulatory Interactions by Computing Technologies
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    Chapter 10 The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy.
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    Chapter 11 Genomic Regulation of MicroRNA Expression in Disease Development
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    Chapter 12 Next-Generation Sequencing for MicroRNA Expression Profile
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    Chapter 13 Handling High-Dimension (High-Feature) MicroRNA Data.
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    Chapter 14 Effective Removal of Noisy Data Via Batch Effect Processing
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    Chapter 15 Logical Reasoning (Inferencing) on MicroRNA Data
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    Chapter 16 Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions
  18. Altmetric Badge
    Chapter 17 Involvement of MicroRNAs in Diabetes and Its Complications
  19. Altmetric Badge
    Chapter 18 MicroRNA Regulatory Networks as Biomarkers in Obesity: The Emerging Role.
  20. Altmetric Badge
    Chapter 19 Expression of MicroRNAs in Thyroid Carcinoma.
Attention for Chapter 10: The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy.
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Chapter title
The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy.
Chapter number 10
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7046-9_10
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

Rasiah Loganantharaj Ph.D., Thomas A. Randall, Rasiah Loganantharaj, Loganantharaj, Rasiah, Randall, Thomas A.

Editors

Jingshan Huang, Glen M. Borchert, Dejing Dou, Jun (Luke) Huan, Wenjun Lan, Ming Tan, Bin Wu

Abstract

MicroRNAs (miRNAs) are small (18-24 nt) endogenous RNAs found across diverse phyla involved in posttranscriptional regulation, primarily downregulation of mRNAs. Experimentally determining miRNA-mRNA interactions can be expensive and time-consuming, making the accurate computational prediction of miRNA targets a high priority. Since miRNA-mRNA base pairing in mammals is not perfectly complementary and only a fraction of the identified motifs are real binding sites, accurately predicting miRNA targets remains challenging. The limitations and bottlenecks of existing algorithms and approaches are discussed in this chapter.A new miRNA-mRNA interaction algorithm was implemented in Python (TargetFind) to capture three different modes of association and to maximize detection sensitivity to around 95% for mouse (mm9) and human (hg19) reference data. For human (hg19) data, the prediction accuracy with any one feature among evolutionarily conserved score, multiple targets in a UTR or changes in free energy varied within a close range from 63.5% to 66%. When the results of these features are combined with majority voting, the expected prediction accuracy increases to 69.5%. When all three features are used together, the average best prediction accuracy with tenfold cross validation from the classifiers naïve Bayes, support vector machine, artificial neural network, and decision tree were, respectively, 66.5%, 67.1%, 69%, and 68.4%. The results reveal the advantages and limitations of these approaches.When comparing different sets of features on their strength in predicting true hg19 targets, evolutionarily conserved score slightly outperformed all other features based on thermostability, and target multiplicity. The sophisticated supervised learning algorithms did not improve the prediction accuracy significantly compared to a simple threshold based approach on conservation score or combining the results of each feature with majority agreements. The targets from randomly generated UTRs behaved similar to that of noninteracting pairs with respect to changes in free energy. Availability of additional experimental data describing noninteracting pairs will advance our understanding of the characteristics and the factors positively and negatively influencing these interactions.

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 20%
Student > Master 2 20%
Student > Ph. D. Student 2 20%
Other 1 10%
Student > Bachelor 1 10%
Other 1 10%
Unknown 1 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 20%
Computer Science 1 10%
Agricultural and Biological Sciences 1 10%
Chemical Engineering 1 10%
Psychology 1 10%
Other 3 30%
Unknown 1 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2018.
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#12,210,318
of 13,791,430 outputs
Outputs from Methods in molecular biology
#6,003
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Outputs of similar age
#225,982
of 267,228 outputs
Outputs of similar age from Methods in molecular biology
#17
of 34 outputs
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