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Cancer Systems Biology

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

Table of Contents

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    Book Overview
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    Chapter 1 Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis
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    Chapter 2 Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data
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    Chapter 3 Analyzing DNA Methylation Patterns During Tumor Evolution
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    Chapter 4 MicroRNA Networks in Breast Cancer Cells
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    Chapter 5 Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels
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    Chapter 6 Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells
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    Chapter 7 Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research
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    Chapter 8 Quantitative Analysis of Tyrosine Kinase Signaling Across Differentially Embedded Human Glioblastoma Tumors
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    Chapter 9 Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles
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    Chapter 10 Stochastic and Deterministic Models for the Metastatic Emission Process: Formalisms and Crosslinks
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    Chapter 11 Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details
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    Chapter 12 Profiling Tumor Infiltrating Immune Cells with CIBERSORT
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    Chapter 13 Systems Biology Approaches in Cancer Pathology
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    Chapter 14 Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing
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    Chapter 15 A Robust Optimization Approach to Cancer Treatment under Toxicity Uncertainty
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    Chapter 16 Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response
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    Chapter 17 Methods for High-throughput Drug Combination Screening and Synergy Scoring
Attention for Chapter 9: Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles
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Chapter title
Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles
Chapter number 9
Book title
Cancer Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7493-1_9
Pubmed ID
Book ISBNs
978-1-4939-7492-4, 978-1-4939-7493-1
Authors

Leslie R. Euceda, Tonje H. Haukaas, Tone F. Bathen, Guro F. Giskeødegård

Abstract

Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 18%
Librarian 1 9%
Lecturer 1 9%
Other 1 9%
Student > Bachelor 1 9%
Other 1 9%
Unknown 4 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Engineering 1 9%
Unknown 6 55%