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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 Dynamic Gaussian Graphical Models for Modelling Genomic Networks
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    Chapter 2 Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes
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    Chapter 3 BioCloud Search EnGene: Surfing Biological Data on the Cloud
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    Chapter 4 Genomic Sequence Classification Using Probabilistic Topic Modeling
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    Chapter 5 Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches
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    Chapter 6 Weighting Scheme Methods for Enhanced Genomic Annotation Prediction
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    Chapter 7 French Flag Tracking by Morphogenetic Simulation Under Developmental Constraints
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    Chapter 8 High–Dimensional Sparse Matched Case–Control and Case–Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies
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    Chapter 9 Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data
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    Chapter 10 Writing Generation Model for Health Care Neuromuscular System Investigation
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    Chapter 11 Clusters Identification in Binary Genomic Data: The Alternative Offered by Scan Statistics Approach
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    Chapter 12 Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods
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    Chapter 13 Integration of Clinico-Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction Systems
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    Chapter 14 Computational Intelligence Methods for Bioinformatics and Biostatistics
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    Chapter 15 Prediction of Single-Nucleotide Polymorphisms Causative of Rare Diseases
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    Chapter 16 A Framework for Mining Life Sciences Data on the Semantic Web in an Interactive, Graph-Based Environment
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    Chapter 17 Combining Not-Proper ROC Curves and Hierarchical Clustering to Detect Differentially Expressed Genes in Microarray Experiments
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    Chapter 18 Fast and Parallel Algorithm for Population-Based Segmentation of Copy-Number Profiles
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    Chapter 19 Identification of Pathway Signatures in Parkinson’s Disease with Gene Ontology and Sparse Regularization
Attention for Chapter 2: Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

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3 X users

Citations

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2 Dimensions

Readers on

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34 Mendeley
Summary X Dimensions citations
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Chapter title
Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes
Chapter number 2
Book title
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published in
Lecture notes in computer science, June 2013
DOI 10.1007/978-3-319-09042-9_2
Book ISBNs
978-3-31-909041-2, 978-3-31-909042-9
Authors

Hossam M. Ashtawy, Nihar R. Mahapatra

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Student > Bachelor 5 15%
Professor 4 12%
Student > Doctoral Student 3 9%
Student > Master 3 9%
Other 4 12%
Unknown 8 24%
Readers by discipline Count As %
Computer Science 5 15%
Agricultural and Biological Sciences 4 12%
Pharmacology, Toxicology and Pharmaceutical Science 4 12%
Biochemistry, Genetics and Molecular Biology 3 9%
Chemistry 3 9%
Other 4 12%
Unknown 11 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 July 2014.
All research outputs
#14,782,907
of 22,758,963 outputs
Outputs from Lecture notes in computer science
#4,542
of 8,126 outputs
Outputs of similar age
#117,422
of 196,766 outputs
Outputs of similar age from Lecture notes in computer science
#61
of 130 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 42nd percentile – i.e., 42% 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 196,766 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 130 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 52% of its contemporaries.

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