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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Cover of 'Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 Variable Genetic Operator Search for the Molecular Docking Problem
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    Chapter 2 Role of Centrality in Network-Based Prioritization of Disease Genes
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    Chapter 3 Parallel Multi-Objective Approaches for Inferring Phylogenies
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    Chapter 4 An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction
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    Chapter 5 Finding Gapped Motifs by a Novel Evolutionary Algorithm
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    Chapter 6 Top-Down Induction of Phylogenetic Trees
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    Chapter 7 A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships
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    Chapter 8 Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci
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    Chapter 9 Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions
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    Chapter 10 Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques
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    Chapter 11 Correlation–Based Scatter Search for Discovering Biclusters from Gene Expression Data
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    Chapter 12 A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination
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    Chapter 13 A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins
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    Chapter 14 Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments
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    Chapter 15 Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models
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    Chapter 16 The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics
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    Chapter 17 Artificial Immune Systems for Epistasis Analysis in Human Genetics
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    Chapter 18 Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models
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    Chapter 19 Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study
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    Chapter 20 Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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    Chapter 21 Investigating Populational Evolutionary Algorithms to Add Vertical Meaning in Phylogenetic Trees
Attention for Chapter 16: The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics
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Chapter title
The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics
Chapter number 16
Book title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published by
Springer, Berlin, Heidelberg, April 2010
DOI 10.1007/978-3-642-12211-8_16
Book ISBNs
978-3-64-212210-1, 978-3-64-212211-8
Authors

Casey S. Greene, Daniel S. Himmelstein, Jeff Kiralis, Jason H. Moore, Greene, Casey S., Himmelstein, Daniel S., Kiralis, Jeff, Moore, Jason H.

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X Demographics

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

Geographical breakdown

Country Count As %
United States 2 12%
Haiti 1 6%
Germany 1 6%
Unknown 13 76%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 47%
Researcher 3 18%
Student > Bachelor 1 6%
Student > Master 1 6%
Professor 1 6%
Other 2 12%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 4 24%
Agricultural and Biological Sciences 4 24%
Biochemistry, Genetics and Molecular Biology 3 18%
Medicine and Dentistry 2 12%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 2 12%
Unknown 1 6%

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