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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Overview of attention for book
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 Association Study between Gene Expression and Multiple Relevant Phenotypes with Cluster Analysis.
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    Chapter 2 Gaussian Graphical Models to Infer Putative Genes Involved in Nitrogen Catabolite Repression in S. cerevisiae
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    Chapter 3 Chronic Rat Toxicity Prediction of Chemical Compounds Using Kernel Machines
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    Chapter 4 Simulating Evolution of Drosophila Melanogaster Ebony Mutants Using a Genetic Algorithm
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    Chapter 5 Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform
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    Chapter 6 F-score with Pareto Front Analysis for Multiclass Gene Selection
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    Chapter 7 A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms
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    Chapter 8 Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks
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    Chapter 9 Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease
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    Chapter 10 On the Efficiency of Local Search Methods for the Molecular Docking Problem
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    Chapter 11 A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems
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    Chapter 12 Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets
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    Chapter 13 Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective
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    Chapter 14 Clustering Metagenome Short Reads Using Weighted Proteins
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    Chapter 15 A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony
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    Chapter 16 Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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    Chapter 17 Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancer Classification
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
3 Mendeley
Summary Blogs X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published by
Lecture notes in computer science, April 2009
DOI 10.1007/978-3-642-01184-9
Pubmed ID
19578549
ISBNs
978-3-64-201183-2, 978-3-64-201184-9
Authors

Turner SD, Ritchie MD, Bush WS

Editors

Pizzuti, Clara, Ritchie, Marylyn D., Giacobini, Mario

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 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 %
Student > Bachelor 1 33%
Researcher 1 33%
Student > Master 1 33%
Readers by discipline Count As %
Computer Science 1 33%
Physics and Astronomy 1 33%
Energy 1 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 20 January 2017.
All research outputs
#3,773,122
of 23,321,213 outputs
Outputs from Lecture notes in computer science
#869
of 8,159 outputs
Outputs of similar age
#14,574
of 94,560 outputs
Outputs of similar age from Lecture notes in computer science
#3
of 19 outputs
Altmetric has tracked 23,321,213 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,159 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 94,560 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.

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