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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
  2. Altmetric Badge
    Chapter 1 Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics
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    Chapter 2 Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs
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    Chapter 3 Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction
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    Chapter 4 Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data
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    Chapter 5 One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification
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    Chapter 6 Understanding Signal Sequences with Machine Learning
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    Chapter 7 Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization
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    Chapter 8 Modeling Genetic Networks: Comparison of Static and Dynamic Models
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    Chapter 9 A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data
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    Chapter 10 Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation
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    Chapter 11 Evolutionary Search for Improved Path Diagrams
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    Chapter 12 Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment
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    Chapter 13 Towards Evolutionary Network Reconstruction Tools for Systems Biology
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    Chapter 14 A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites
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    Chapter 15 Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks
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    Chapter 16 Tuning ReliefF for Genome-Wide Genetic Analysis
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    Chapter 17 Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms
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    Chapter 18 Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines
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    Chapter 19 Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines
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    Chapter 20 Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives
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    Chapter 21 Virtual Error: A New Measure for Evolutionary Biclustering
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    Chapter 22 Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters
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    Chapter 23 Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes
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    Chapter 24 The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms
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    Chapter 25 Classification of Cell Fates with Support Vector Machine Learning
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    Chapter 26 Reconstructing Linear Gene Regulatory Networks
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    Chapter 27 Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment
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    Chapter 28 Substitution Matrix Optimisation for Peptide Classification
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
2 X users
wikipedia
3 Wikipedia pages

Readers on

mendeley
24 Mendeley
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Title
Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics
Published by
Lecture notes in computer science, January 2007
DOI 10.1007/978-3-540-71783-6
ISBNs
978-3-54-071782-9, 978-3-54-071783-6
Authors

Marchiori, E, Moore, Jason H, Rajapakse, Jagath Chandana

Editors

Elena Marchiori, Jason H. Moore, Jagath C. Rajapakse

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

Geographical breakdown

Country Count As %
Israel 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 8%
Student > Bachelor 1 4%
Other 1 4%
Professor > Associate Professor 1 4%
Unknown 19 79%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 13%
Business, Management and Accounting 1 4%
Engineering 1 4%
Unknown 19 79%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 31 January 2020.
All research outputs
#6,283,415
of 22,953,506 outputs
Outputs from Lecture notes in computer science
#2,053
of 8,133 outputs
Outputs of similar age
#34,875
of 157,452 outputs
Outputs of similar age from Lecture notes in computer science
#22
of 59 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 8,133 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 74% 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 157,452 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 77% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.