Biomedical Text Mining

Chapter 1 Biomedical Literature Mining and Its Components

Chapter 2 Text Mining Protocol to Retrieve Significant Drug–Gene Interactions from PubMed Abstracts

Chapter 3 A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining

Chapter 4 Finding Gene Associations by Text Mining and Annotating it with Gene Ontology

Chapter 5 Biomedical Literature Mining for Repurposing Laboratory Tests

Chapter 6 A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources

Chapter 7 Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries

Chapter 8 A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature

Chapter 9 Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed

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

Chapter 11 A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning

Chapter 12 BioBERT and Similar Approaches for Relation Extraction

Chapter 13 A Text Mining Protocol for Predicting Drug–Drug Interaction and Adverse Drug Reactions from PubMed Articles

Chapter 14 A Text Mining Protocol for Extracting Drug–Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease

Chapter 15 Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Chapter 16 Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis