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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 An Open-Source Tool for Managing Time-Evolving Variant Annotation
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    Chapter 2 Extracting Few Representative Reconciliations with Host Switches
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    Chapter 3 A Quantitative and Qualitative Characterization of k -mer Based Alignment-Free Phylogeny Construction
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    Chapter 4 Cancer Mutational Signatures Identification with Sparse Dictionary Learning
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    Chapter 5 Icing : Large-Scale Inference of Immunoglobulin Clonotypes
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    Chapter 6 Adenine : A HPC-Oriented Tool for Biological Data Exploration
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    Chapter 7 Disease–Genes Must Guide Data Source Integration in the Gene Prioritization Process
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    Chapter 8 Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions
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    Chapter 9 GP-Based Grammatical Inference for Classification of Amyloidogenic Sequences
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    Chapter 10 Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance
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    Chapter 11 Haplotype and Repeat Separation in Long Reads
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    Chapter 12 Tumor Subclonal Progression Model for Cancer Hallmark Acquisition
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    Chapter 13 GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics
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    Chapter 14 Structural Features of a DPPG Liposome Layer Adsorbed on a Rough Surface
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    Chapter 15 Chemical Exchanges and Actuation in Liposome-Based Synthetic Cells: Interaction with Biological Cells
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    Chapter 16 A Nano Communication System for CTC Detection in Blood Vessels
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    Chapter 17 Experimental Evidences Suggest High Between-Vesicle Diversity of Artificial Vesicle Populations: Results, Models and Implications
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    Chapter 18 Towards the Synthesis of Photo-Autotrophic Protocells
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    Chapter 19 Hierarchical Block Matrix Approach for Multi-view Clustering
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Title
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published by
Springer International Publishing, April 2019
DOI 10.1007/978-3-030-14160-8
ISBNs
978-3-03-014159-2, 978-3-03-014160-8
Editors

Bartoletti, Massimo, Barla, Annalisa, Bracciali, Andrea, Klau, Gunnar W., Peterson, Leif, Policriti, Alberto, Tagliaferri, Roberto

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 20%
Student > Bachelor 1 20%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 40%
Biochemistry, Genetics and Molecular Biology 1 20%
Unknown 2 40%