Biomedical Text Mining

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.