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Computational Systems Biology

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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 21: Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
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Chapter title
Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
Chapter number 21
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_21
Pubmed ID
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Heng Xu, Yang Shu

Abstract

Treatment outcomes for acute lymphoblastic leukemia (ALL), especially pediatric ALL, have greatly improved due to the risk-adapted therapy. Combination of drug development, clinical practice, as well as basic genetic researches has brought the survival rate of ALL from less than 10% to more than 90% today, not only increasing the treatment efficacy but also limiting adverse drug reactions (ADRs). In this review, we summarized the landscape identification of ALL genetic alterations, which provided the opportunity to increase the survival rate and especially minimize the relapse risk of ALL, and highlighted the importance of the development of new technologies of genomic investigation for translational medicine.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 16%
Student > Bachelor 2 8%
Lecturer 2 8%
Student > Postgraduate 2 8%
Other 1 4%
Other 5 20%
Unknown 9 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 24%
Medicine and Dentistry 4 16%
Agricultural and Biological Sciences 2 8%
Computer Science 1 4%
Social Sciences 1 4%
Other 1 4%
Unknown 10 40%