{"title":"Literature-Based Discoveries in Lupus Treatment","authors":"Oluwaseyi Jaiyeoba, Vetria Byrd","volume":189,"journal":"International Journal of Health and Medical Engineering","pagesStart":124,"pagesEnd":133,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10012682","abstract":"<p>Systemic lupus erythematosus (aka lupus) is a chronic disease known for its chameleon-like ability to mimic symptoms of other diseases rendering it hard to detect, diagnose and treat. The heterogeneous nature of the disease generates disparate data that are often multifaceted and multi-dimensional. Musculoskeletal manifestation of lupus is one of the most common clinical manifestations of lupus. This research links disparate literature on the treatment of lupus as it affects the musculoskeletal system using the discoveries from literature-based research articles available on the PubMed database. Several Natural Language Processing (NPL) tools exist to connect disjointed but related literature, such as Connected Papers, Bitola, and Gopalakrishnan. Literature-based discovery (LBD) has been used to bridge unconnected disciplines based on text mining procedures. The technical\/medical literature consists of many technical\/medical concepts, each having its&nbsp; sub-literature. This approach has been used to link Parkinson\u2019s, Raynaud, and Multiple Sclerosis treatment within works of literature.&nbsp; Literature-based discovery methods can connect two or more related but disjointed literature concepts to produce a novel and plausible approach to solving a research problem. Data visualization techniques with the help of natural language processing tools are used to visually represent the result of literature-based discoveries. Literature search results can be voluminous, but Data visualization processes can provide insight and detect subtle patterns in large data. These insights and patterns can lead to discoveries that would have otherwise been hidden from disjointed literature. In this research, literature data are mined and combined with visualization techniques for heterogeneous data to discover viable treatments reported in the literature for lupus expression in the musculoskeletal system. This research answers the question of using literature-based discovery to identify potential treatments for a multifaceted disease like lupus. A three-pronged methodology is used in this research: text mining, natural language processing, and data visualization. These three research-related fields are employed to identify patterns in lupus-related data that, when visually represented, could aid research in the treatment of lupus. This work introduces a method for visually representing interconnections of various lupus-related literature. The methodology outlined in this work is the first step toward literature-based research and treatment planning for the musculoskeletal manifestation of lupus. The results also outline the interconnection of complex, disparate data associated with the manifestation of lupus in the musculoskeletal system. The societal impact of this work is broad. Advances in this work will improve the quality of life for millions of persons in the workforce currently diagnosed and silently living with a musculoskeletal disease associated with lupus.<\/p>","references":"[1] K. Mahmoud, A. Zayat, and E. M. Vital, \u201dMusculoskeletal manifestations\r\nof systemic lupus erythmatosus,\u201d Current opinion in rheumatology, vol.\r\n29, pp. 486-492, 2017.\r\n[2] T. D\u00a8orner and R. Furie, \u201dNovel paradigms in systemic lupus\r\nerythematosus,\u201d The Lancet, vol. 393, pp. 2344-2358, 2019.\r\n[3] S. Yavuz and P. E. Lipsky, \u201dCurrent Status of the Evaluation and\r\nManagement of Lupus Patients and Future Prospects,\u201d Frontiers in\r\nmedicine, vol. 8, 2021.\r\n[4] E. Heiman, S. S. Lim, G. Bao, and C. Drenkard, \u201dDepressive symptoms\r\nare associated with low treatment adherence in African American\r\nindividuals with systemic lupus erythematosus,\u201d Journal of clinical\r\nrheumatology: practical reports on rheumatic & musculoskeletal diseases,\r\nvol. 24, p. 368, 2018.\r\n[5] G. R. V. Hughes, Lupus: The Facts: Oxford University Press, 2000.\r\n[6] A. Stockl, \u201dComplex syndromes, ambivalent diagnosis, and existential\r\nuncertainty: the case of Systemic Lupus Erythematosus (SLE),\u201d Social\r\nscience & medicine, vol. 65, pp. 1549-1559, 2007.\r\n[7] S. R. Johnson, R. Brinks, K. H. Costenbader, D. Daikh, M. Mosca,\r\nR. Ramsey-Goldman, et al., \u201dPerformance of the 2019 EULAR\/ACR\r\nclassification criteria for systemic lupus erythematosus in early disease,\r\nacross sexes and ethnicities,\u201d Annals of the rheumatic diseases, vol. 79,\r\npp. 1333-1339, 2020.\r\n[8] M. E. Orme, A. Voreck, R. Aksouh, R. Ramsey-Goldman, and M. W.\r\nSchreurs, \u201dSystematic review of anti-dsDNA testing for systemic lupus\r\nerythematosus: a meta-analysis of the diagnostic test specificity of an\r\nanti-dsDNA fluorescence enzyme immunoassay,\u201d Autoimmunity Reviews,\r\nvol. 20, p. 102943, 2021.\r\n[9] C. Arrigoni, S. Lopa, C. Candrian, and M. Moretti, \u201dOrgans-on-a-chip\r\nas model systems for multifactorial musculoskeletal diseases,\u201d Current\r\nopinion in biotechnology, vol. 63, pp. 79-88, 2020.\r\n[10] K. Mahmoud, A. Zayat, and E. M. Vital, \u201dMusculoskeletal\r\nmanifestations of systemic lupus erythmatosus,\u201d Current opinion in\r\nrheumatology, vol. 29, pp. 486-492, 2017.\r\n[11] R. Johnson, A. Watkinson, and M. Mabe, \u201dThe STM report: An\r\noverview of scientific and scholarly publishing,\u201d International Association\r\nof Scientific, Technical and Medical Publishers, pp. 1-214, 2018.\r\n[12] S. Henry and B. T. McInnes, \u201dLiterature based discovery: models,\r\nmethods, and trends,\u201d Journal of biomedical informatics, vol. 74, pp.\r\n20-32, 2017.\r\n[13] H.-T. Yang, J.-H. Ju, Y.-T. Wong, I. Shmulevich, and J.-H. Chiang, \u201dLiterature-based discovery of new candidates for drug repurposing,\u201d\r\nBriefings in bioinformatics, vol. 18, pp. 488-497, 2017.\r\n[14] S. Henry, D. S. Wijesinghe, A. Myers, and B. T. McInnes, \u201dUsing\r\nliterature based discovery to gain insights into the metabolomic processes\r\nof cardiac arrest,\u201d Frontiers in Research Metrics and Analytics, vol. 6, p.\r\n32, 2021.\r\n[15] S. Pyysalo, S. Baker, I. Ali, S. Haselwimmer, T. Shah, A. Young, et\r\nal., \u201dLION LBD: a literature-based discovery system for cancer biology,\u201d\r\nBioinformatics, vol. 35, pp. 1553-1561, 2019.\r\n[16] Y. Zhu, W. Jung, F. Wang, and C. Che, \u201dDrug repurposing against\r\nParkinson\u2019s disease by text mining the scientific literature,\u201d Library Hi\r\nTech, 2020.\r\n[17] G. Crichton, S. Baker, Y. Guo, and A. Korhonen, \u201dNeural networks\r\nfor open and closed Literature-based Discovery,\u201d Plos one, vol. 15, p.\r\ne0232891, 2020.\r\n[18] A. Usai, M. Pironti, M. Mital, and C. A. Mejri, \u201dKnowledge discovery\r\nout of text data: a systematic review via text mining,\u201d Journal of\r\nknowledge management, 2018.\r\n[19] V. Gopalakrishnan, K. Jha, W. Jin, and A. Zhang, \u201dA survey on literature\r\nbased discovery approaches in biomedical domain,\u201d Journal of biomedical\r\ninformatics, vol. 93, p. 103141, 2019.\r\n[20] M. Weeber, J. A. Kors, and B. Mons, \u201dOnline tools to support\r\nliterature-based discovery in the life sciences,\u201d Briefings in\r\nbioinformatics, vol. 6, pp. 277-286, 2005.\r\n[21] M. Weeber, R. Vos, H. Klein, L. T. de Jong-van den Berg, A. R.\r\nAronson, and G. Molema, \u201dGenerating hypotheses by discovering implicit\r\nassociations in the literature: a case report of a search for new potential\r\ntherapeutic uses for thalidomide,\u201d Journal of the American Medical\r\nInformatics Association, vol. 10, pp. 252-259, 2003.\r\n[22] P. Srinivasan and B. Libbus, \u201dMining MEDLINE for implicit links\r\nbetween dietary substances and diseases,\u201d Bioinformatics, vol. 20, pp.\r\ni290-i296, 2004.\r\n[23] Z. Dai, Q. Li, G. Yang, Y. Wang, Y. Liu, Z. Zheng, et al., \u201dUsing\r\nliterature-based discovery to identify candidate genes for the interaction\r\nbetween myocardial infarction and depression,\u201d BMC Medical Genetics,\r\nvol. 20, pp. 1-10, 2019.\r\n[24] A. Lenci, \u201dDistributional models of word meaning,\u201d Annual review of\r\nLinguistics, vol. 4, pp. 151-171, 2018.\r\n[25] P. Bruza, R. Cole, D. Song, and Z. Bari, \u201dTowards operational abduction\r\nfrom a cognitive perspective,\u201d Logic Journal of IGPL, vol. 14, pp.\r\n161-177, 2006.\r\n[26] M. C. Ganiz, W. M. Pottenger, and C. D. Janneck, \u201dRecent advances\r\nin literature based discovery,\u201d Journal of the American Society for\r\nInformation Science and Technology, JASIST (Submitted), 2005.\r\n[27] C. M. Miller, T. C. Rindflesch, M. Fiszman, D. Hristovski, D. Shin, G.\r\nRosemblat, et al., \u201dA closed literature-based discovery technique finds a\r\nmechanistic link between hypogonadism and diminished sleep quality in\r\naging men,\u201d Sleep, vol. 35, pp. 279-285, 2012.\r\n[28] M. Thilakaratne, K. Falkner, and T. Atapattu, \u201dA systematic review on\r\nliterature-based discovery: general overview, methodology, & statistical\r\nanalysis,\u201d ACM Computing Surveys (CSUR), vol. 52, pp. 1-34, 2019.\r\n[29] C. E. Lipscomb, \u201dMedical subject headings (MeSH),\u201d Bulletin of the\r\nMedical Library Association, vol. 88, p. 265, 2000.\r\n[30] H. Kilicoglu, G. Rosemblat, M. Fiszman, and D. Shin, \u201dBroad-coverage\r\nbiomedical relation extraction with SemRep,\u201d BMC bioinformatics, vol.\r\n21, pp. 1-28, 2020.\r\n[31] R. N. Kostoff and U. Patel, \u201dLiterature-related discovery and innovation:\r\nChronic kidney disease,\u201d Technological Forecasting and Social Change,\r\nvol. 91, pp. 341-351, 2015.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 189, 2022"}