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Bioinformatics

Overview of attention for book
Cover of 'Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.
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    Chapter 2 Inferring Function from Homology.
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    Chapter 3 Inferring Functional Relationships from Conservation of Gene Order.
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    Chapter 4 Structural and Functional Annotation of Long Noncoding RNAs.
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    Chapter 5 Construction of Functional Gene Networks Using Phylogenetic Profiles.
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    Chapter 6 Inferring Genome-Wide Interaction Networks.
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    Chapter 7 Integrating Heterogeneous Datasets for Cancer Module Identification.
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    Chapter 8 Metabolic Pathway Mining.
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    Chapter 9 Analysis of Genome-Wide Association Data.
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    Chapter 10 Adjusting for Familial Relatedness in the Analysis of GWAS Data.
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    Chapter 11 Analysis of Quantitative Trait Loci.
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    Chapter 12 High-Dimensional Profiling for Computational Diagnosis.
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    Chapter 13 Molecular Similarity Concepts for Informatics Applications.
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    Chapter 14 Compound Data Mining for Drug Discovery.
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    Chapter 15 Studying Antibody Repertoires with Next-Generation Sequencing.
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    Chapter 16 Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.
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    Chapter 17 Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.
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    Chapter 18 Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets.
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    Chapter 19 Clustering.
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    Chapter 20 Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.
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    Chapter 21 Information Visualization for Biological Data.
Attention for Chapter 7: Integrating Heterogeneous Datasets for Cancer Module Identification.
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Chapter title
Integrating Heterogeneous Datasets for Cancer Module Identification.
Chapter number 7
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6613-4_7
Pubmed ID
Book ISBNs
978-1-4939-6611-0, 978-1-4939-6613-4
Authors

A. K. M. Azad

Editors

Jonathan M. Keith

Abstract

The availability of multiple heterogeneous high-throughput datasets provides an enabling resource for cancer systems biology. Types of data include: Gene expression (GE), copy number aberration (CNA), miRNA expression, methylation, and protein-protein Interactions (PPI). One important problem that can potentially be solved using such data is to determine which of the possible pair-wise interactions among genes contributes to a range of cancer-related events, from tumorigenesis to metastasis. It has been shown by various studies that applying integrated knowledge from multi-omics datasets elucidates such complex phenomena with higher statistical significance than using a single type of dataset individually. However, computational methods for processing multiple data types simultaneously are needed. This chapter reviews some of the computational methods that use integrated approaches to find cancer-related modules.

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Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 33%
Unknown 2 67%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 33%
Unknown 2 67%
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 23 January 2018.
All research outputs
#18,483,671
of 22,903,988 outputs
Outputs from Methods in molecular biology
#7,927
of 13,133 outputs
Outputs of similar age
#310,539
of 420,462 outputs
Outputs of similar age from Methods in molecular biology
#691
of 1,074 outputs
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So far Altmetric has tracked 13,133 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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