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Bioinformatics in MicroRNA Research

Overview of attention for book
Cover of 'Bioinformatics in MicroRNA Research'

Table of Contents

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    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
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    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 16: Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions
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Chapter title
Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions
Chapter number 16
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, May 2017
DOI 10.1007/978-1-4939-7046-9_16
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

Singh, Sumi, Benton, Ryan G., Singh, Anurag, Singh, Anshuman, Sumi Singh, Ryan G. Benton Ph.D., Anurag Singh, Anshuman Singh, Ryan G. Benton

Editors

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

Abstract

In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies. This literature review briefly discusses recent machine learning-based techniques applied in the discovery of miRNAs, prediction of miRNA targets, and inference of miRNA functions. We also discuss the limitations of how these approaches have been elucidated in previous studies.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Professor > Associate Professor 1 17%
Student > Bachelor 1 17%
Student > Master 1 17%
Unknown 1 17%
Readers by discipline Count As %
Chemical Engineering 1 17%
Biochemistry, Genetics and Molecular Biology 1 17%
Agricultural and Biological Sciences 1 17%
Computer Science 1 17%
Psychology 1 17%
Other 0 0%
Unknown 1 17%

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 26 May 2017.
All research outputs
#9,714,937
of 10,993,399 outputs
Outputs from Methods in molecular biology
#5,111
of 7,569 outputs
Outputs of similar age
#220,342
of 264,670 outputs
Outputs of similar age from Methods in molecular biology
#16
of 32 outputs
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