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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 Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI
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    Chapter 2 Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis
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    Chapter 3 Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram
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    Chapter 4 The First in-silico Model of Leg Movement Activity During Sleep
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    Chapter 5 Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia
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    Chapter 6 Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features
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    Chapter 7 High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methods
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    Chapter 8 A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity
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    Chapter 9 A Rule-Based Approach for Generating Synthetic Biological Pathways
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    Chapter 10 Machine Learning Classifiers Based on Dimensionality Reduction Techniques for the Early Diagnosis of Alzheimer’s Disease Using Magnetic Resonance Imaging and Positron Emission Tomography Brain Data
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    Chapter 11 Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations
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    Chapter 12 Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria
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    Chapter 13 RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification
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    Chapter 14 Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models
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    Chapter 15 Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data
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    Chapter 16 The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions
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    Chapter 17 Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences
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    Chapter 18 Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data
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    Chapter 19 Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers
Attention for Chapter 17: Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences
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Chapter title
Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences
Chapter number 17
Book title
Computational Intelligence Methods for Bioinformatics and Biostatistics
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-20837-9_17
Book ISBNs
978-3-03-120836-2, 978-3-03-120837-9
Authors

Crossman, Lisa C.

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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 > Doctoral Student 1 20%
Lecturer > Senior Lecturer 1 20%
Unknown 3 60%
Readers by discipline Count As %
Computer Science 1 20%
Unknown 4 80%