A cloud based knowledge discovery framework, for medicinal plants from PubMed literature

Informatics in Medicine Unlocked - Tập 16 - Trang 100105 - 2019
Niyati Kumari Behera1, G.S. Mahalakshmi1
1Department of Computer Science and Engg, Anna University, India

Tài liệu tham khảo

Zhu, 2009, Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity, Bioinformatics, 25, 1944, 10.1093/bioinformatics/btp338 Gu, 2013, Efficient semisupervised MEDLINE document clustering with MeSH-semantic and global-content constraints, IEEE Trans Cybern, 43, 1265, 10.1109/TSMCB.2012.2227998 Yea, 2016, A data mining approach to selecting herbs with similar efficacy: targeted selection methods based on medical subject headings (MeSH), J Ethnopharmacol, 182, 27, 10.1016/j.jep.2016.02.007 Yu, 2016, Improving the utility of MeSH® terms using the Topical MeSH representation, J Biomed Inf, 61, 77, 10.1016/j.jbi.2016.03.013 Yetisgen-Yildiz, 2006, Using statistical and knowledge-based approaches for literature-based discovery, J Biomed Inf, 39, 600, 10.1016/j.jbi.2005.11.010 Wonjun Choi and Hyunju Lee, A text mining approach for identifying herb-chemical relationships from biomedical articles, DTMBIO '15 Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics, 2015, 25-25. Dr. Duke's phytochemical and ethnobotanical databases,”https://phytochem.nal.usda.gov/phytochem/search. https://www.webmd.com/default.htm. https://www.ncbi.nlm.nih.gov/pubmed/. https://meshb.nlm.nih.gov/search. Choi, 2016, A corpus for plant-chemical relationships in the biomedical domain, BMC Bioinf, 17, 386, 10.1186/s12859-016-1249-5 Jenson, 2014, Integrated text mining and Chemoinformatics analysis associates diet to health benefit at molecular level, PLoS Comput Biol, 10, 1 Li, 2015, Annotating chemicals, diseases and their interactions in biomedical literature, 173 Wijaya, 2014, Supervised clustering based on DPClusO: prediction of plant-disease relations using Jamu formulas of KNApSAcK database, BioMed Res Int, 2014, 10.1155/2014/831751 Ye, 2011, HIT: linking herbal active ingredients to targets, Nucleic Acids Res, 39, D1055, 10.1093/nar/gkq1165 http://www.vqol.com/2017/07/03/pubmed-cloud-448611222.html. Lim-Cheng, 2014, Semi-automatic population of ontology of philippine medicinal plants from on-line text, 6 Gonzalez, 2016, Recent advances and emerging applications in text and data mining for biomedical discovery, Briefings Bioinf, 17, 33, 10.1093/bib/bbv087 Feng, 2015, Discovery of acupoints and combinations with potential to treat vascular dementia: a data mining analysis. Evid Based Complement, Alternat Med, 2015, 310591 Shergis, 2015, Natural products for chronic cough: text mining the East Asian historical literature for future therapeutics, Chron Respir Dis, 12, 204, 10.1177/1479972315583043 Zhang, 2014, Text mining of the classical medical literature for medicines that show potential in diabetic nephropathy. Evid Based Complement, Alternat Med2014, 189125 Selvaraj, 2016, Indian medicinal plants for diabetes: text data mining the literature of different electronic databases for future therapeutics, Biomed Res, S430 Xu, 2013, Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing, BMC Bioinf, 14, 181, 10.1186/1471-2105-14-181 Zhou, 2010, Text mining for traditional Chinese medical knowledge discovery: a survey, J Biomed Inf, 43, 650, 10.1016/j.jbi.2010.01.002 Choi, 2015, Establishment of a comprehensive list of candidate antiaging medicinal herb used in Korean medicine by text mining of the classical Korean medical literature, ‘Dongeuibogam’, and preliminary evaluation of the anti-aging effects of these herb, Evid base Compl Alternative Med, 2015 Rosario, 2004, Classifying semantic relations in bioscience texts, 430 Anbarkhan, 2018, Text mining approach to extract associations between obesity and Arabic herbal plants, 211 Mohanraj, 2018, IMPPAT: A curated database of Indian Medicinal Plants, Phytochem Therapeut Sci Rep, 8