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Computational Intelligence Methods for Bioinformatics and Biostatistics

Overview of attention for book
Cover of 'Computational Intelligence Methods for Bioinformatics and Biostatistics'

Table of Contents

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    Book Overview
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    Chapter 1 Coarse-Grained Modeling of the HIV–1 Protease Binding Mechanisms: I. Targeting Structural Flexibility of the Protease Flaps and Implications for Drug Design
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    Chapter 2 Coarse-Grained Modeling of the HIV–1 Protease Binding Mechanisms: II. Folding Inhibition
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    Chapter 3 Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data
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    Chapter 4 Comparative In Silico Evaluation of MYB Transcription Factors in Eucalyptus, Sugarcane and Rice Transcriptomes
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    Chapter 5 Building Maps of Drugs Mode-of-Action from Gene Expression Data
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    Chapter 6 In Silico Evaluation of Osmoprotectants in Eucalyptus Transcriptome
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    Chapter 7 Mining Association Rule Bases from Integrated Genomic Data and Annotations
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    Chapter 8 Stability and Performances in Biclustering Algorithms
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    Chapter 9 Splice Site Prediction Using Artificial Neural Networks
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    Chapter 10 Interval Length Analysis in Multi Layer Model
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    Chapter 11 A Multivariate Algorithm for Gene Selection Based on the Nearest Neighbor Probability
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    Chapter 12 Control of Cellular Glycolysis by Perturbations in the Glucose Influx
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    Chapter 13 Curating a Large-Scale Regulatory Network by Evaluating Its Consistency with Expression Datasets
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    Chapter 14 Spatial Clustering of Molecular Dynamics Trajectories in Protein Unfolding Simulations
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    Chapter 15 Clustering Bacteria Species Using Neural Gas: Preliminary Study
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    Chapter 16 A New Linear Initialization in SOM for Biomolecular Data
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    Chapter 17 3D Volume Reconstruction and Biometric Analysis of Fetal Brain from MR Images
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    Chapter 18 Searching for Glycomics Role in Stem Cell Development
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    Chapter 19 A New Protein Representation Based on Fragment Contacts: Towards an Improvement of Contact Maps Predictions
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    Chapter 20 Analysis of Kernel Based Protein Classification Strategies Using Pairwise Sequence Alignment Measures
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    Chapter 21 Topology Preserving Neural Networks for Peptide Design in Drug Discovery
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    Chapter 22 A Machine Learning Approach to Mass Spectra Classification with Unsupervised Feature Selection
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    Chapter 23 Liver i-Biopsy TM and the Corresponding Intelligent Fibrosis Scoring Systems: i-Metavir F and i-Ishak F
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    Chapter 24 An Extension of the TIGR M4 Suite to Preprocess and Visualize Affymetrix Binary Files
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    Chapter 25 A Supervised Learning Technique and Its Applications to Computational Biology
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    Chapter 26 A Visualization ToolKit Based Application for Representing Macromolecular Surfaces
Overall attention for this book and its chapters
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Mentioned by

wikipedia
3 Wikipedia pages

Readers on

mendeley
4 Mendeley
citeulike
2 CiteULike
Summary Wikipedia
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
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published by
ADS, June 2009
DOI 10.1007/978-3-642-02504-4
ISBNs
978-3-64-202503-7, 978-3-64-202504-4
Editors

Masulli, Francesco, Tagliaferri, Roberto, Verkhivker, Gennady M.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Professor > Associate Professor 2 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 50%
Biochemistry, Genetics and Molecular Biology 1 25%
Mathematics 1 25%
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 05 June 2020.
All research outputs
#7,553,524
of 23,041,514 outputs
Outputs from ADS
#9,313
of 37,451 outputs
Outputs of similar age
#37,870
of 112,164 outputs
Outputs of similar age from ADS
#98
of 272 outputs
Altmetric has tracked 23,041,514 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 37,451 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 29th percentile – i.e., 29% 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 112,164 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 272 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.

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