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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 Automatic Task Decomposition for the NeuroEvolution of Augmenting Topologies (NEAT) Algorithm
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    Chapter 2 Evolutionary Reaction Systems
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    Chapter 3 Optimizing the Edge Weights in Optimal Assignment Methods for Virtual Screening with Particle Swarm Optimization
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    Chapter 4 Lévy-Flight Genetic Programming: Towards a New Mutation Paradigm
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    Chapter 5 Understanding Zooplankton Long Term Variability through Genetic Programming
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    Chapter 6 Inferring Disease-Related Metabolite Dependencies with a Bayesian Optimization Algorithm
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    Chapter 7 A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series
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    Chapter 8 Tracking the Evolution of Cooperation in Complex Networked Populations
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    Chapter 9 GeNet: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks
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    Chapter 10 Comparing Multiobjective Artificial Bee Colony Adaptations for Discovering DNA Motifs
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    Chapter 11 The Role of Mutations in Whole Genome Duplication
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    Chapter 12 Comparison of Methods for Meta-dimensional Data Analysis Using in Silico and Biological Data Sets
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    Chapter 13 Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithm
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    Chapter 14 Prediction of Mitochondrial Matrix Protein Structures Based on Feature Selection and Fragment Assembly
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    Chapter 15 Feature Selection for Lung Cancer Detection Using SVM Based Recursive Feature Elimination Method
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    Chapter 16 Measuring Gene Expression Noise in Early Drosophila Embryos: The Highly Dynamic Compartmentalized Micro-environment of the Blastoderm Is One of the Main Sources of Noise
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    Chapter 17 Artificial Immune Systems Perform Valuable Work When Detecting Epistasis in Human Genetic Datasets
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    Chapter 18 A Biologically Informed Method for Detecting Associations with Rare Variants
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    Chapter 19 Complex Detection in Protein-Protein Interaction Networks: A Compact Overview for Researchers and Practitioners
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    Chapter 20 Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor
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    Chapter 21 A NSGA-II Algorithm for the Residue-Residue Contact Prediction
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    Chapter 22 In Silico Infection of the Human Genome
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    Chapter 23 Improving Phylogenetic Tree Interpretability by Means of Evolutionary Algorithms
Attention for Chapter 7: A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series
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Mentioned by

wikipedia
3 Wikipedia pages

Readers on

mendeley
30 Mendeley
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Chapter title
A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series
Chapter number 7
Book title
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Published in
Lecture notes in computer science, April 2012
DOI 10.1007/978-3-642-29066-4_7
Book ISBNs
978-3-64-229065-7, 978-3-64-229066-4
Authors

Marco S. Nobile, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri, Dario Pescini, Nobile, Marco S., Besozzi, Daniela, Cazzaniga, Paolo, Mauri, Giancarlo, Pescini, Dario

Editors

Mario Giacobini, Leonardo Vanneschi, William S. Bush

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
Malta 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 30%
Student > Master 6 20%
Researcher 5 17%
Student > Doctoral Student 2 7%
Student > Postgraduate 2 7%
Other 3 10%
Unknown 3 10%
Readers by discipline Count As %
Computer Science 13 43%
Agricultural and Biological Sciences 7 23%
Engineering 5 17%
Physics and Astronomy 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 0 0%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 February 2024.
All research outputs
#7,451,942
of 22,782,096 outputs
Outputs from Lecture notes in computer science
#2,485
of 8,124 outputs
Outputs of similar age
#53,002
of 161,761 outputs
Outputs of similar age from Lecture notes in computer science
#6
of 18 outputs
Altmetric has tracked 22,782,096 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 55% 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 161,761 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.