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.
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.
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