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Computational Advances in Bio and Medical Sciences

Overview of attention for book
Cover of 'Computational Advances in Bio and Medical Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization
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    Chapter 2 Graph Representation Learning for Protein Conformation Sampling
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    Chapter 3 Excerno: Filtering Mutations Caused by the Clinical Archival Process in Sequencing Data
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    Chapter 4 Relabeling Metabolic Pathway Data with Groups to Improve Prediction Outcomes
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    Chapter 5 MELEPS: Multiple Expert Linear Epitope Prediction System
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    Chapter 6 Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
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    Chapter 7 Unified SAT-Solving for Hard Problems of Phylogenetic Network Construction
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    Chapter 8 Feature Selection for Identification of Risk Factors Associated with Infant Mortality
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    Chapter 9 Addressing Classification on Highly Imbalanced Clinical Datasets
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    Chapter 10 mcPBWT: Space-Efficient Multi-column PBWT Scanning Algorithm for Composite Haplotype Matching
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    Chapter 11 Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data
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    Chapter 12 Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics
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    Chapter 13 An SVM Based Approach to Study the Racial Disparity in Triple-Negative Breast Cancer
Attention for Chapter 9: Addressing Classification on Highly Imbalanced Clinical Datasets
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4 Mendeley
Summary
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Chapter title
Addressing Classification on Highly Imbalanced Clinical Datasets
Chapter number 9
Book title
Computational Advances in Bio and Medical Sciences
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-17531-2_9
Book ISBNs
978-3-03-117530-5, 978-3-03-117531-2
Authors

Fonseca, Alexandre Babilone, Martins-Jr, David Correa, Wicik, Zofia, Postula, Marek, Simões, Sérgio Nery

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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 %
Student > Ph. D. Student 1 25%
Researcher 1 25%
Unknown 2 50%
Readers by discipline Count As %
Materials Science 1 25%
Engineering 1 25%
Unknown 2 50%

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