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Bioinformatics in MicroRNA Research

Overview of attention for book
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
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    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 12: Next-Generation Sequencing for MicroRNA Expression Profile
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Chapter title
Next-Generation Sequencing for MicroRNA Expression Profile
Chapter number 12
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, May 2017
DOI 10.1007/978-1-4939-7046-9_12
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

Hu, Yue, Lan, Wenjun, Miller, Daniel, Yue Hu, Wenjun Lan Ph.D., Daniel Miller, Wenjun Lan

Editors

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

Abstract

Sequencing technologies have made considerable advancements. From the Sanger sequencing method to the next-generation sequencing (NGS) methods, and from the NGS methods to the third-generation sequencing methods, we can see the development thread of the sequencing technology. Currently, NGS is the main contender in the sequencing market. NGS technologies provide an opportunity to research the microRNA (miRNA) expression profiles in detail. The NGS platforms have their own special characteristics, but share some main ideas. DNA sequencing via NGS is fundamental for RNA sequencing and miRNA sequencing. MiRNA sequencing has special characteristics. The pipeline of miRNA sequencing by NGS is explained in detail from the wet experiment to the dry experiment.

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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Student > Bachelor 7 21%
Researcher 4 12%
Student > Doctoral Student 2 6%
Student > Master 2 6%
Other 3 9%
Unknown 8 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 32%
Medicine and Dentistry 3 9%
Agricultural and Biological Sciences 2 6%
Computer Science 2 6%
Mathematics 1 3%
Other 7 21%
Unknown 8 24%

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
#8,739,341
of 10,993,399 outputs
Outputs from Methods in molecular biology
#3,822
of 7,569 outputs
Outputs of similar age
#189,392
of 264,670 outputs
Outputs of similar age from Methods in molecular biology
#11
of 32 outputs
Altmetric has tracked 10,993,399 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,569 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.