A Tool for Suggesting Ayurvedic Remedies from Curated and Classified Clinical Trial Reports

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    It requires great effort to search through huge number of published articles that provide information we need. Therefore it is necessary to find a solution that helps researchers in gaining accurate and deep understanding about diseases. Thus drug discovery and drug repurposing are gaining significance with the current onics tools. Traditional Medical practices like Ayurveda needs to be more visible to practitioners with evidence based approach. The clinical trials conducted have to be shared with the world for attaining the very philosophy of Ayurveda.. This paper presents a survey on various text mining technologies developed to classify theories and literature pertaining to the clinical observations of practitioners and suggests a possible solution to match a patient’s symptoms.



  • Keywords

    Drug discovery; MeSH based Text mining method; Network pharmacology; Text Mining

  • References

      [1] A. Friboulet and D. Thomas, “Systems biology—an interdisciplinary approach”, Biosensors and Bioelectronics, vol. 20, no. 12, pp. 2404–2407, 2005.

      [2] J. T. Metz and P. J. Hajduk,” Rational approaches to targeted polypharmacology: creating and navigating protein ligand interaction networks,” Current Opinion in Chemical Biology, vol. 14, no. 4, pp. 498–504, 2010.

      [3] A. L. Hopkins, “Network pharmacology,” Nature Biotechnology, vol. 25, no. 10, pp. 1110–1111, 2007.

      [4] Bhushan Patwardhan, :Bridging Ayurveda with evidence-based scientific approaches in medicine”, The EPMA Journal 2014, 5:19

      [5] U Chandran, N Mehendale, G Tillu, B Patwardhan, “Network Pharmacology of Ayurveda Formulation Triphala with Special Reference to Anti-Cancer Property”, Combinatorial Chemistry & High Throughput screening, 2015

      [6] Peter Csermely, Tamás Korcsmáros, Huba J.M. Kiss, Gábor London, Nussinov,” Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review”,Pharmacol Ther. 2013 June ; 138(3): 333–408. doi:10.1016/j.pharmthera.2013.01.016.

      [7] Nir Yosef, Lior Ungar, Einat Zalckvar, Adi Kimchi, Martin Kupiec, Eytan Ruppin and Roded Sharan “Toward accurate reconstruction of functional protein networks” Molecular Systems Biology 5:248; doi:10.1038/msb.2009.3

      [8] Emre Guney, Jo¨rg Menche, Marc Vidal and Albert-La´szlo´ Bara´basi “Network-based in silico drug efficacy screening” Nat. Commun. 2016; 7:10331–10343.

      [9] G. Zheng, J. Zhan, H. Guo, M. Jiang, C. Lu, and A. Lu, “Exploring associated rules of Danggui in traditional Chinese medicine through text mining,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, pp. 198–203, 2013.

      [10] . S. Bleik, M. Song, A. Smalter, J. Huan, and G. Lushington, “CGM: A biomedical text categorization approach using concept graph mining,” 2009 IEEE Int. Conf. Bioinforma. Biomed. Work., pp. 38–43, 2009.

      [11] . L. Gong, R. Yan, Q. Liu, H. Yang, G. Yang, and K. Jiang, “Extraction of biomedical information related to breast cancer using text mining,” 2016 12th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov. ICNC-FSKD 2016, pp. 801–805, 2016.

      [12] . Q. Jiang, H. Li, and J. Liang, “Free text mining of TCM medical records based on conditional random fields,” pp. 0–5, 2016.

      [13] . Z. Jiang, L. Li, D. Huang, and L. Jin, “2015- Training_word_embeddings_for_deep_learning_in_biomedical_text_mining_tasks-1,” pp. 625–628, 2015.

      [14] T. Evangelidis and L. Xie, “An integrated workflow for proteome-wide off-target identification and polypharmacology drug design,” Tsinghua Sci. Technol., vol. 19, no. 3, pp. 275–284, 2014.

      [15] Y. Kocyigit and H. Seker, “Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2014, pp. 812–815, 2014.

      [16] L. Wu, Y. Shen, M. Li, and F. X. Wu, “Network Output Controllability-Based Method for Drug Target Identification,” IEEE Trans. Nanobioscience, vol. 14, no. 2, pp. 184–191, 2015.

      [17] A. Koohi, H. Homayoun, J. Xu, and M. Orooji, “Co-clustering of diseases, genes, and drugs for identification of their related gene modules,” Proc. 8th Int. Conf. Adv. Comput. Intell. ICACI 2016, pp. 407–411, 2016.

      [18] D. Harnie et al., “Scaling machine learning for target prediction in drug discovery using Apache Spark,” Futur. Gener. Comput. Syst., vol. 67, pp. 409–417, 2017.

      [19] . L. Wu, L. Tang, M. Li, J. Wang, and F.-X. Wu, “Biomolecular Network Controllability With Drug Binding Information.,” IEEE Trans. Nanobioscience, vol. 16, no. 5, pp. 326–332, 2017.

      [20] U. Chandran, N. Mehendale, G. Tillu, and B. Patwardhan, “Network pharmacology of ayurveda formulation triphala with special reference to anti-cancer property,” Comb. Chem. High Throughput Screen., vol. 18, no. 9, pp. 846–854, 2015.

      [21] C. O. Tudor, K. Vijay-Shanker, and C. J. Schmidt, “Mining gene-related information from biomedical literature,” Proc. - 2009 IEEE Int. Conf. Bioinforma. Biomed. Work. BIBMW 2009, p. 342, 2009.

      [22] M. Krauthammer, M. D. Pauline, K. Phd, and C. Friedman, “Linking protein interaction data to the MESH hierarchy,” p. 5027, 2001.

      [23] A. J. Jimeno-Yepes, L. Plaza, J. G. Mork, A. R. Aronson, and A. Díaz, “MeSH indexing based on automatically generated summaries.,” BMC Bioinformatics, vol. 14, p. 208, 2013.

      [24] Z. Huang, “Mining disease associated biomarker networks from PubMed,” Int. Conf. Syst. Biol. ISB, pp. 15–18, 2013.

      [25] G. Shan, Y. Lu, B. Min, W. Qu, and C. Zhang, “A MeSH-based text mining method for identifying novel prebiotics.,” Medicine (Baltimore)., vol. 95, no. 49, p. e5585, 2016.

      [26] . H. Caniza, A. E. Romero, and A. Paccanaro, “A network medicine approach to quantify distance between hereditary disease modules on the interactome,” Sci. Rep., vol. 5, no. 1, p. 17658, 2016.

      [27] K. Jensen, G. Panagiotou, and I. Kouskoumvekaki, “Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level,” PLoS Comput. Biol., vol. 10, no. 1, 2014.

      [28] H. G. G. Vaka and S. Mukhopadhyay, “Hypotheses generation pertaining to ayurveda using automated vocabulary generation and transitive text mining,” NBiS 2009 - 12th Int. Conf. Network-Based Inf. Syst., pp. 200–205, 2009




Article ID: 20038
DOI: 10.14419/ijet.v7i4.5.20038

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.