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Computational Systems Biology

Overview of attention for book
Cover of 'Computational Systems Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 DNA Sequencing Data Analysis
  3. Altmetric Badge
    Chapter 2 Transcriptome Sequencing: RNA-Seq
  4. Altmetric Badge
    Chapter 3 Capture Hybridization of Long-Range DNA Fragments for High-Throughput Sequencing
  5. Altmetric Badge
    Chapter 4 The Introduction and Clinical Application of Cell-Free Tumor DNA
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    Chapter 5 Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data
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    Chapter 6 An Overview of Genome-Wide Association Studies
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    Chapter 7 Integrative Analysis of Omics Big Data
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    Chapter 8 The Reconstruction and Analysis of Gene Regulatory Networks
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    Chapter 9 Differential Coexpression Network Analysis for Gene Expression Data
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    Chapter 10 iSeq: Web-Based RNA-seq Data Analysis and Visualization
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    Chapter 11 Revisit of Machine Learning Supported Biological and Biomedical Studies
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    Chapter 12 Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
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    Chapter 13 Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces
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    Chapter 14 Computational Prediction of Protein O-GlcNAc Modification
  16. Altmetric Badge
    Chapter 15 Machine Learning-Based Modeling of Drug Toxicity
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    Chapter 16 Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
  18. Altmetric Badge
    Chapter 17 Single-Cell Protein Assays: A Review
  19. Altmetric Badge
    Chapter 18 Data Analysis in Single-Cell Transcriptome Sequencing
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    Chapter 19 Applications of Single-Cell Sequencing for Multiomics
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    Chapter 20 Progress on Diagnosis of Tuberculous Meningitis
  22. Altmetric Badge
    Chapter 21 Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
Attention for Chapter 12: Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog

Citations

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4 Dimensions

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18 Mendeley
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Chapter title
Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
Chapter number 12
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_12
Pubmed ID
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Wei Lan, Liyu Huang, Dehuan Lai, Qingfeng Chen, Lan, Wei, Huang, Liyu, Lai, Dehuan, Chen, Qingfeng

Abstract

With the development and improvement of next-generation sequencing technology, a great number of noncoding RNAs have been discovered. Long noncoding RNAs (lncRNAs) are the biggest kind of noncoding RNAs with more than 200 nt nucleotides in length. There are increasing evidences showing that lncRNAs play key roles in many biological processes. Therefore, the mutation and dysregulation of lncRNAs have close association with a number of complex human diseases. Identifying the most likely interaction between lncRNAs and diseases becomes a fundamental challenge in human health. A common view is that lncRNAs with similar function tend to be related to phenotypic similar diseases. In this chapter, we firstly introduce the concept of lncRNA, their biological features, and available data resources. Further, the recent computational approaches are explored to identify interactions between long noncoding RNAs and diseases, including their advantages and disadvantages. The key issues and potential future works of predicting interactions between long noncoding RNAs and diseases are also discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 17%
Student > Bachelor 2 11%
Student > Doctoral Student 2 11%
Student > Master 2 11%
Student > Ph. D. Student 2 11%
Other 0 0%
Unknown 7 39%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 39%
Agricultural and Biological Sciences 2 11%
Mathematics 1 6%
Medicine and Dentistry 1 6%
Unknown 7 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 19 March 2018.
All research outputs
#5,810,623
of 23,028,364 outputs
Outputs from Methods in molecular biology
#1,637
of 13,175 outputs
Outputs of similar age
#115,244
of 442,381 outputs
Outputs of similar age from Methods in molecular biology
#145
of 1,499 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 13,175 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 86% 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 442,381 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 73% of its contemporaries.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.