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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
Attention for Chapter 13: Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective
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Citations

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Chapter title
Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective
Chapter number 13
Book title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published in
Lecture notes in computer science, January 2009
DOI 10.1007/978-3-642-01184-9_13
Book ISBNs
978-3-64-201183-2, 978-3-64-201184-9
Authors

Pedro Evangelista, Isabel Rocha, Eugénio C. Ferreira, Miguel Rocha, Eugénio C. Ferreira

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X Demographics

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

Geographical breakdown

Country Count As %
United Kingdom 1 7%
Portugal 1 7%
Unknown 13 87%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 20%
Student > Doctoral Student 2 13%
Student > Bachelor 2 13%
Student > Ph. D. Student 2 13%
Researcher 2 13%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Chemical Engineering 2 13%
Biochemistry, Genetics and Molecular Biology 2 13%
Agricultural and Biological Sciences 2 13%
Computer Science 2 13%
Economics, Econometrics and Finance 1 7%
Other 2 13%
Unknown 4 27%
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 28 April 2013.
All research outputs
#15,270,698
of 22,708,120 outputs
Outputs from Lecture notes in computer science
#4,645
of 8,122 outputs
Outputs of similar age
#141,553
of 168,827 outputs
Outputs of similar age from Lecture notes in computer science
#104
of 177 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,122 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 168,827 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.

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