Biomedical Text Mining
Springer-Verlag New York Inc.
978-1-0716-2307-7 (ISBN)
Authoritative and cutting-edge, Biomedical Text Mining aims to be a useful practical guide to researches to help further their studies.
lt;p>1. Biomedical literature mining and its components
Kalpana Raja
2. Text mining protocol to retrieve significant drug-gene interactions from PubMed abstracts
Sadhanha Anand, Oviya Ramalakshmi Iyyappan, Sharanya Manoharan, Dheepa Anand, Manonmani Alvin Jose, and Raja Ravi Shankar
3. A hybrid protocol for finding novel gene targets for various diseases using microarray expression data analysis and text mining
Sharanya Manoharan and Oviya Ramalakshmi Iyyappan
4. Finding gene associations by text mining and annotating it with Gene Ontology
Oviya Ramalakshmi Iyyappan and Sharanya Manoharan
5. Biomedical literature mining for repurposing laboratory tests
Finn Kuusisto, Ross Kleiman, and Jeremy Weiss
6. A simple computational approach to identify potential drugs for multiple sclerosis and cognitive disorders from expert curated resources
Kalpana Raja, Archana Prabahar, and Shyam Sundar Arputhanatham
7. Combining literature mining and machine learning for predicting biomedical discoveries
Balu Bhasuran
8. A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature
Sabenabanu Abdulkadhar and Jeyakumar Natarajan
9. Text mining and machine learning protocol for extracting human related protein phosphorylation information from PubMed
Krishnamurthy Arumugam and Raja Ravi Shankar
10. 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
Krishnamurthy Arumugam, Malathi Sellappan, Dheepa Anand, Sadhanha Anand, and Subhashini Vedagiri Radhakishnan
11. A hybrid protocol for identifying comorbidity-based potential drugs for COVID-19 using biomedical literature mining, network analysis, and deep learning
Archana Prabahar and Anbumathi Palanisamy
12. BioBERT and Similar Approaches for Relation Extraction
Balu Bhasuran
13. A text mining protocol for predicting drug-drug interaction and adverse drug reactions from PubMed articles
Mohamed Saleem Abdul Shukkoor, Kalpana Raja, and Mohamad Taufik Hidayat Bin Baharuldin
14. A text mining protocol for extracting drug-drug interaction and adverse drug reactions specific to patient population, pharmacokinetics, pharmacodynamics, and disease
Mohamed Saleem Abdul Shukkoor, Mohamad Taufik Hidayat Bin Baharuldin, and Kalpana Raja
15. Extracting significant comorbid diseases from MeSH index of PubMed
Dheepa Anand, Sharanya Manoharan, Oviya Ramalakshmi Iyyappan, Sadhanha Anand, and Kalpana Raja
16. Integration of transcriptomic data and metabolomic data using biomedical literature mining and pathway analysis
Archana Prabahar
Erscheinungsdatum | 21.06.2023 |
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Reihe/Serie | Methods in Molecular Biology |
Zusatzinfo | 76 Illustrations, color; 3 Illustrations, black and white; XI, 321 p. 79 illus., 76 illus. in color. |
Verlagsort | New York, NY |
Sprache | englisch |
Maße | 178 x 254 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
Informatik ► Weitere Themen ► Bioinformatik | |
Medizin / Pharmazie ► Medizinische Fachgebiete ► Biomedizin | |
Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie | |
Schlagworte | BioBert • Drug-drug interaction • gene expression omnibus • Metabolomics • Transcriptomics |
ISBN-10 | 1-0716-2307-9 / 1071623079 |
ISBN-13 | 978-1-0716-2307-7 / 9781071623077 |
Zustand | Neuware |
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