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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 A Commentary on a Censored Regression Estimator
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    Chapter 2 Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability
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    Chapter 3 Comparison of Gene Expression Signature Using Rank Based Statistical Inference
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    Chapter 4 Managing NGS Differential Expression Uncertainty with Fuzzy Sets
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    Chapter 5 Module Detection in Dynamic Networks by Temporal Edge Weight Clustering
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    Chapter 6 A Novel Technique for Reduction of False Positives in Predicted Gene Regulatory Networks
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    Chapter 7 Unsupervised Trajectory Inference Using Graph Mining
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    Chapter 8 Supervised Term Weights for Biomedical Text Classification: Improvements in Nearest Centroid Computation
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    Chapter 9 Alignment Free Dissimilarities for Nucleosome Classification
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    Chapter 10 A Deep Learning Approach to DNA Sequence Classification
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    Chapter 11 Clustering Protein Structures with Hadoop
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    Chapter 12 Comparative Analysis of MALDI-TOF Mass Spectrometric Data in Proteomics: A Case Study
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    Chapter 13 Binary Particle Swarm Optimization Versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees
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    Chapter 14 Computing Discrete Fine-Grained Representations of Protein Surfaces
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    Chapter 15 The Challenges of Interpreting Phosphoproteomics Data: A Critical View Through the Bioinformatics Lens
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    Chapter 16 Bioinformatics Challenges and Potentialities in Studying Extreme Environments
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    Chapter 17 Improving Genome Assemblies Using Multi-platform Sequence Data
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    Chapter 18 Validation Pipeline for Computational Prediction of Genomics Annotations
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    Chapter 19 Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data
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    Chapter 20 NuchaRt: Embedding High-Level Parallel Computing in R for Augmented Hi-C Data Analysis
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    Chapter 21 A Web Resource on Skeletal Muscle Transcriptome of Primates
Attention for Chapter 18: Validation Pipeline for Computational Prediction of Genomics Annotations
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Citations

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Chapter title
Validation Pipeline for Computational Prediction of Genomics Annotations
Chapter number 18
Book title
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published in
Lecture notes in computer science, July 2016
DOI 10.1007/978-3-319-44332-4_18
Book ISBNs
978-3-31-944331-7, 978-3-31-944332-4
Authors

Davide Chicco, Marco Masseroli

Editors

Claudia Angelini, Paola MV Rancoita, Stefano Rovetta

View on publisher site
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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 33%
Researcher 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Unknown 1 33%
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 08 August 2016.
All research outputs
#18,467,278
of 22,882,389 outputs
Outputs from Lecture notes in computer science
#6,013
of 8,129 outputs
Outputs of similar age
#282,327
of 365,393 outputs
Outputs of similar age from Lecture notes in computer science
#309
of 452 outputs
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So far Altmetric has tracked 8,129 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 452 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.

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