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Computational Systems Biology

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

Table of Contents

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    Book Overview
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    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 1: DNA Sequencing Data Analysis
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Citations

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Chapter title
DNA Sequencing Data Analysis
Chapter number 1
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_1
Pubmed ID
29536434
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Keyi Long, Lei Cai, Lin He

Abstract

Among various biological data, DNA sequence is doubtlessly a fundamental datum. By obtaining particular DNA sequence data and analyzing, biologists get to understand life science more precisely. This chapter is an overview of DNA sequencing technology and its data analysis methods, providing information about DNA sequencing, several different methods, and tools applied in data analysis. Both advantages and disadvantages are discussed.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 388 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 2%
United Kingdom 3 <1%
Chile 2 <1%
China 2 <1%
Italy 2 <1%
Netherlands 1 <1%
Malaysia 1 <1%
France 1 <1%
Finland 1 <1%
Other 10 3%
Unknown 356 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 113 29%
Student > Ph. D. Student 79 20%
Student > Master 51 13%
Student > Bachelor 37 10%
Other 19 5%
Other 54 14%
Unknown 35 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 189 49%
Biochemistry, Genetics and Molecular Biology 80 21%
Medicine and Dentistry 19 5%
Computer Science 14 4%
Immunology and Microbiology 8 2%
Other 41 11%
Unknown 37 10%

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