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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
  5. Altmetric Badge
    Chapter 4 MicroRNAs: Biomarkers, Diagnostics, and Therapeutics
  6. Altmetric Badge
    Chapter 5 Relational Databases and Biomedical Big Data
  7. Altmetric Badge
    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
  10. Altmetric Badge
    Chapter 9 Exploring MicroRNA::Target Regulatory Interactions by Computing Technologies
  11. Altmetric Badge
    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
  16. Altmetric Badge
    Chapter 15 Logical Reasoning (Inferencing) on MicroRNA Data
  17. Altmetric Badge
    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 5: Relational Databases and Biomedical Big Data
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
7 tweeters

Citations

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4 Dimensions

Readers on

mendeley
14 Mendeley
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Chapter title
Relational Databases and Biomedical Big Data
Chapter number 5
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, May 2017
DOI 10.1007/978-1-4939-7046-9_5
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

de Silva, N. H. Nisansa D., N. H. Nisansa D. de Silva

Editors

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

Abstract

In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. More often than not, the default and classical solution to this in the biomedical domain according to past research is relational databases. While this used to be the norm for a long while, it is evident that there is a trend to move away from relational databases in favor of other types and paradigms of databases. However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 29%
Student > Ph. D. Student 2 14%
Researcher 2 14%
Student > Master 2 14%
Lecturer 1 7%
Other 1 7%
Unknown 2 14%
Readers by discipline Count As %
Computer Science 3 21%
Biochemistry, Genetics and Molecular Biology 3 21%
Medicine and Dentistry 3 21%
Chemical Engineering 1 7%
Psychology 1 7%
Other 1 7%
Unknown 2 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 January 2021.
All research outputs
#5,264,603
of 16,634,238 outputs
Outputs from Methods in molecular biology
#1,520
of 9,650 outputs
Outputs of similar age
#98,581
of 273,426 outputs
Outputs of similar age from Methods in molecular biology
#3
of 32 outputs
Altmetric has tracked 16,634,238 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 9,650 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 83% of its peers.
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 273,426 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
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 done particularly well, scoring higher than 93% of its contemporaries.