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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
  17. Altmetric Badge
    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
  20. Altmetric Badge
    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 16: Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
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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

dimensions_citation
4 Dimensions

Readers on

mendeley
40 Mendeley
Summary Blogs Dimensions citations
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Chapter title
Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
Chapter number 16
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_16
Pubmed ID
29536449
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Jing Cheng, Wenxian Lan, Guangyong Zheng, Xianfu Gao

Abstract

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 18%
Student > Bachelor 7 18%
Student > Ph. D. Student 5 13%
Researcher 3 8%
Lecturer 2 5%
Other 7 18%
Unknown 9 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 20%
Biochemistry, Genetics and Molecular Biology 8 20%
Medicine and Dentistry 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 3 8%
Computer Science 3 8%
Other 5 13%
Unknown 9 23%
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 21 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.

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