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Biomedical Text Mining

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Cover of 'Biomedical Text Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Biomedical Literature Mining and Its Components
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    Chapter 2 Text Mining Protocol to Retrieve Significant Drug–Gene Interactions from PubMed Abstracts
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    Chapter 3 A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining
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    Chapter 4 Finding Gene Associations by Text Mining and Annotating it with Gene Ontology
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    Chapter 5 Biomedical Literature Mining for Repurposing Laboratory Tests
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    Chapter 6 A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources
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    Chapter 7 Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries
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    Chapter 8 A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature
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    Chapter 9 Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed
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    Chapter 10 A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination
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    Chapter 11 A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning
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    Chapter 12 BioBERT and Similar Approaches for Relation Extraction
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    Chapter 13 A Text Mining Protocol for Predicting Drug–Drug Interaction and Adverse Drug Reactions from PubMed Articles
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    Chapter 14 A Text Mining Protocol for Extracting Drug–Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease
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    Chapter 15 Extracting Significant Comorbid Diseases from MeSH Index of PubMed
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    Chapter 16 Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis
Attention for Chapter 10: A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination
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Chapter title
A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination
Chapter number 10
Book title
Biomedical Text Mining
Published by
Humana, New York, NY, June 2022
DOI 10.1007/978-1-0716-2305-3_10
Pubmed ID
35713865
Book ISBNs
978-1-07-162304-6, 978-1-07-162305-3
Authors

Krishnamurthy Arumugam, Malathi Sellappan, Dheepa Anand, Sadhanha Anand, Subhashini Vedagiri Radhakrishnan, Arumugam, Krishnamurthy, Sellappan, Malathi, Anand, Dheepa, Anand, Sadhanha, Radhakrishnan, Subhashini Vedagiri

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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 %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 33%
Computer Science 1 33%
Unknown 1 33%

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