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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
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    Chapter 2 Transcriptome Sequencing: RNA-Seq
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    Chapter 3 Capture Hybridization of Long-Range DNA Fragments for High-Throughput Sequencing
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    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
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    Chapter 15 Machine Learning-Based Modeling of Drug Toxicity
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    Chapter 16 Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
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    Chapter 17 Single-Cell Protein Assays: A Review
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    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
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    Chapter 21 Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
Attention for Chapter 5: Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data
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Chapter title
Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data
Chapter number 5
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_5
Pubmed ID
29536438
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Chen, Shifu, Liu, Ming, Zhou, Yanqing, Shifu Chen, Ming Liu, Yanqing Zhou

Abstract

As a major biomarker of liquid biopsy, cell-free tumor DNA (ctDNA), which can be extracted from blood, urine, or other circulating liquids, is able to provide comprehensive genetic information of tumor and better overcome the tumor heterogeneity problem comparing to tissue biopsy. Developed in recent years, next-generation sequencing (NGS) is a widely used technology for analyzing ctDNA. Although the technologies of processing ctDNA samples are mature, the task to detect low mutated allele frequency (MAF) variations from noisy sequencing data remains challenging. In this chapter, the authors will first explain the difficulties of analyzing ctDNA sequencing data, review related technologies, and then present some novel bioinformatics methods for analyzing ctDNA NGS data in better ways.

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X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Bachelor 5 12%
Student > Ph. D. Student 5 12%
Other 4 9%
Student > Master 4 9%
Other 4 9%
Unknown 12 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 28%
Agricultural and Biological Sciences 5 12%
Medicine and Dentistry 5 12%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Computer Science 2 5%
Other 3 7%
Unknown 14 33%
Attention Score in Context

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 15 March 2018.
All research outputs
#20,468,008
of 23,026,672 outputs
Outputs from Methods in molecular biology
#9,951
of 13,170 outputs
Outputs of similar age
#378,217
of 442,370 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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So far Altmetric has tracked 13,170 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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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 is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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