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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 12: High-Dimensional Profiling for Computational Diagnosis.
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Chapter title
High-Dimensional Profiling for Computational Diagnosis.
Chapter number 12
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6613-4_12
Pubmed ID
Book ISBNs
978-1-4939-6611-0, 978-1-4939-6613-4
Authors

Claudio Lottaz, Wolfram Gronwald, Rainer Spang, Julia C. Engelmann, Lottaz, Claudio

Editors

Jonathan M. Keith

Abstract

New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study.

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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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 8%
Other 1 8%
Lecturer 1 8%
Student > Doctoral Student 1 8%
Student > Bachelor 1 8%
Other 5 38%
Unknown 3 23%
Readers by discipline Count As %
Medicine and Dentistry 3 23%
Biochemistry, Genetics and Molecular Biology 1 8%
Mathematics 1 8%
Unspecified 1 8%
Computer Science 1 8%
Other 3 23%
Unknown 3 23%
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 13 December 2016.
All research outputs
#20,355,479
of 22,903,988 outputs
Outputs from Methods in molecular biology
#9,922
of 13,133 outputs
Outputs of similar age
#355,350
of 420,462 outputs
Outputs of similar age from Methods in molecular biology
#845
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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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