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Computational Intelligence Methods for Bioinformatics and Biostatistics

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Cover of 'Computational Intelligence Methods for Bioinformatics and Biostatistics'

Table of Contents

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    Book Overview
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    Chapter 1 GO-WAR: A Tool for Mining Weighted Association Rules from Gene Ontology Annotations
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    Chapter 2 Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation
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    Chapter 3 Statistical Analysis of Protein Structural Features: Relationships and PCA Grouping
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    Chapter 4 Exploring the Relatedness of Gene Sets
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    Chapter 5 Consensus Clustering in Gene Expression
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    Chapter 6 Automated Detection of Fluorescent Probes in Molecular Imaging
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    Chapter 7 Applications of Network-based Survival Analysis Methods for Pathways Detection in Cancer
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    Chapter 8 Improving Literature-Based Discovery with Advanced Text Mining
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    Chapter 9 A New Feature Selection Methodology for K-mers Representation of DNA Sequences
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    Chapter 10 Detecting Overlapping Protein Communities in Disease Networks
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    Chapter 11 Approximate Abelian Periods to Find Motifs in Biological Sequences
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    Chapter 12 Sem Best Shortest Paths for the Characterization of Differentially Expressed Genes
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    Chapter 13 The General Regression Neural Network to Classify Barcode and mini-barcode DNA
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    Chapter 14 Transcriptator: Computational Pipeline to Annotate Transcripts and Assembled Reads from RNA-Seq Data
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    Chapter 15 Application of a New Ridge Estimator of the Inverse Covariance Matrix to the Reconstruction of Gene-Gene Interaction Networks
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    Chapter 16 Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction
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    Chapter 17 Use of q-values to Improve a Genetic Algorithm to Identify Robust Gene Signatures
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    Chapter 18 Drug Repurposing by Optimizing Mining of Genes Target Association
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    Chapter 19 The Importance of the Regression Model in the Structure-Based Prediction of Protein-Ligand Binding
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    Chapter 20 The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity
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    Chapter 21 Computational Intelligence Methods for Bioinformatics and Biostatistics
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    Chapter 22 Data-Intensive Computing Infrastructure Systems for Unmodified Biological Data Analysis Pipelines
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    Chapter 23 A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for Computational and Systems Biology Applications
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    Chapter 24 GPGPU Implementation of a Spiking Neuronal Circuit Performing Sparse Recoding
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    Chapter 25 NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data
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    Chapter 26 Erratum to: A New Feature Selection Methodology for K-mers Representation of DNA Sequences
Attention for Chapter 14: Transcriptator: Computational Pipeline to Annotate Transcripts and Assembled Reads from RNA-Seq Data
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Citations

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Chapter title
Transcriptator: Computational Pipeline to Annotate Transcripts and Assembled Reads from RNA-Seq Data
Chapter number 14
Book title
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published by
Springer, Cham, June 2014
DOI 10.1007/978-3-319-24462-4_14
Book ISBNs
978-3-31-924461-7, 978-3-31-924462-4
Authors

Kumar Parijat Tripathi, Daniela Evangelista, Raffaele Cassandra, Mario R. Guarracino, Tripathi, Kumar Parijat, Evangelista, Daniela, Cassandra, Raffaele, Guarracino, Mario R.

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 3 75%
Student > Ph. D. Student 1 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 50%
Unspecified 1 25%
Unknown 1 25%

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