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This volume details step-by-step instructions on biomedical literature mining protocols. Chapters guide readers through various topics such as, disease comorbidity, literature-based discovery, protocols to combine literature mining, machine learning for predicting biomedical discoveries, and uncovering unknown public knowledge by combining two pieces of information from different sets of PubMed articles. Additional chapters discuss the importance of data science to understand outbreaks such as COVID-19. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.
Authoritative and cutting-edge, Biomedical Text Mining aims to be a useful practical guide to researches to help further their studies.
List of contents
Biomedical literature mining and its components.- Text mining protocol to retrieve significant drug-gene interactions from PubMed abstracts.- A hybrid protocol for finding novel gene targets for various diseases using microarray expression data analysis and text mining.- Finding gene associations by text mining and annotating it with Gene Ontology.- Biomedical literature mining for repurposing laboratory tests.- A simple computational approach to identify potential drugs for multiple sclerosis and cognitive disorders from expert curated resources.- Combining literature mining and machine learning for predicting biomedical discoveries.- A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature.- Text mining and machine learning protocol for extracting human related protein phosphorylation information from PubMed.- A text mining and machine learning protocol for extracting post translational modifications of proteins from PubMed: A special focus on glycosylation, acetylation, methylation, hydroxylation, and ubiquitination.- A hybrid protocol for identifying comorbidity-based potential drugs for COVID-19 using biomedical literature mining, network analysis, and deep learning.- BioBERT and Similar Approaches for Relation Extraction.- A text mining protocol for predicting drug-drug interaction and adverse drug reactions from PubMed articles.- A text mining protocol for extracting drug-drug interaction and adverse drug reactions specific to patient population, pharmacokinetics, pharmacodynamics, and disease.- Extracting significant comorbid diseases from MeSH index of PubMed.- Integration of transcriptomic data and metabolomic data using biomedical literature mining and pathway analysis.
