한국정보기술학회논문지 (The Journal of Korean Institute of Information Technology)

한국정보기술학회 (Korean Institute of Information Technology)
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유전자를 중간 매개로 고려한 동시발생 기반의 약물-질병 관계 추론

Co-occurrence Based Drug-disease Relationship Inference with Genes as Mediators

  • 신상원 (가천대학교 컴퓨터공학과) ;
  • 신예은 (가천대학교 컴퓨터공학과) ;
  • 장기업 (가천대학교 IT융합공학과) ;
  • 윤영미 (가천대학교 컴퓨터공학과)
  • 투고 : 2018.08.28
  • 심사 : 2018.11.05
  • 발행 : 2018.11.30
⟨ 이전 논문 다음 논문 ⟩

초록

신약 재창출은 현재 사용되는 약물의 새로운 용도를 발견하는 방법이다. 텍스트 마이닝은 정형화되지 않은 문서로부터 의미 있는 지식을 획득하는 과정을 의미한다. 본 논문에서는 약물-유전자와 유전자-질병에서 동시에 측정된 유전자 출현 빈도의 비율을 고려하여 새로운 약물-질병 관계를 추론하는 방법을 제안한다. 생물학적 문헌으로부터 약물-유전자와 유전자-질병의 동시출현 빈도를 측정하고 각 약물과 질병에 대하여 유전자의 출현 비율을 계산한다. 약물-질병 관계의 가중치는 동시에 측정된 유전자 출현 비율의 평균을 이용하여 계산되고 이를 이용하여 각 질병의 분류 정확도를 측정한다. 약물-질병 관계를 추론하는 것에서 동시출현 빈도를 문장 단위로 측정하고 여러 관계를 고려하는 방법이 기존 방법보다 더 정확히 식별해내는 것을 보였다.

Drug repositioning is to discover new uses of drugs. Text mining derives knowledge from unstructured text. We propose a method to predict new drug-disease relationships by taking into account the rate of frequency of genes simultaneously measured in disease-gene and gene-drug. Co-occurrence of drug-gene and gene-disease in the biological literature is counted and calculate the rate of the gene for each drug and disease. Weights of drug-disease relationships are calculated using the average of the rates of genes that are measured and used to measure the accuracy for each disease. In measuring drug-disease relationships, a more accurate identification of relationships was shown by measuring the frequency on a sentence and considering multiple relationships than existing method.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. Bruce Booth and Rodney Zemmel, "Prospects for productivity", Nature Reviews Drug Discovery, Vol. 3, No. 5, pp. 451-456, May. 2004. https://doi.org/10.1038/nrd1384
  2. DiMasi Josph A, "New drug development in the United States from 1963 to 1999", Clinical Pharmacology & Therapeutics, Vol. 69, No. 5, pp. 286-296, May. 2001. https://doi.org/10.1067/mcp.2001.115132
  3. Tokens Ross, "An overview of the drug development process", Physician executive, Vol. 31, No. 3, pp. 48-52, May/June. 2005.
  4. Jiao Li, Si Zheng, Bin Chen, Atul J.Butte, S. Joshua Swamidass, and Zhiuong Lu, "A survey of current trends in computational drug repositioning", Briefings in Bioinformatics, Vol. 17, No. 1, pp. 2-12, Jan. 2016. https://doi.org/10.1093/bib/bbv020
  5. Douglas E. Jorenby, Scott J. Leischow, Mitchell A. Nides, Stephen I. Rennard, J. Andrew Johnston, Arlene R. Hughes, Stevens S. Smith, Myra L. Muramoto, David M. Daughton, Kimberli Doan, Michael C. Fiore, and Timothy B. Baker, "A Controlled Trial of Sustained-Release Bupropion, a Nicotine Patch, or Both for Smoking Cessation", New England Journal of Medicine, Vol. 340, No. 9, pp. 685-691, Mar. 1999. https://doi.org/10.1056/NEJM199903043400903
  6. Hoe-Yune Jung, Bobae Kim, Hye Guk Ryu, Yosep Ji, Soyoung Park, Seung Hee Choi, Dohyun Lee, In-Kyu Lee, Munki Kim, You Jeong Lee, Woojin Song, Young Hee Lee, Hyung Jin Choi, Chang-Kee Hyun, Wilhelm H Holzapfel, and Kyong-Tai Kim, "Amodiaquine improves insulin resistance and lipid metabolism in diabetic model mice", Diabetes, Obesity and Metabolism, Vol. 20, No. 7, pp. 1688-1701, Mar. 2018. https://doi.org/10.1111/dom.13284
  7. Rachel A. Hodos, Brian A. Kidd, Shameer Khader, Ben P. Readhead, and Joel T. Dudley, "Computational Approaches to Drug Repurposing and Pharmacology", Wiley Interdisciplinary Reviews: Systems biology and medicine, Vol. 8, No. 3, pp. 186-210, May. 2016. https://doi.org/10.1002/wsbm.1337
  8. Ah-Hwee Tan, "Text Mining: The state of the art and the challenges", In Proceedings of the PAKDD 1999 Workshop on Knowledge Discovery from Advanced Databases, Vol. 8, pp. 65-70, 1999.
  9. Ksenya Kveler, Elina Starosvetsky, Amit Ziv-Kenet, Yuval Kalugny, Yuri Gorelik, Gali Shalev-Malul, Netta Aizenbud-Reshef, Tania Dubovik, Mayan Briller, John Campbell, Jan C Rieckmann, Nuaman Asbeh, Doron Rimar, Felix Meissner, Jeff Wiser, and Shai S Shen-Orr, "Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed", Nature Biotechnology, Vol. 36, No. 7, pp. 651-659, July. 2018. https://doi.org/10.1038/nbt.4152
  10. Roger Hale and Chief Operating Officer, "Text Mining: Getting more value from literature resources", Drug Discovery Today, Vol. 10, No. 6, pp. 377-379, Mar. 2005. https://doi.org/10.1016/S1359-6446(05)03409-4
  11. K. Bretonnel Cohen, Lawrence Hunter, "Getting Started in Text Mining", PLoS computational biology, Vol. 4, No. 1, e20, Jan. 2008. https://doi.org/10.1371/journal.pcbi.0040020
  12. Raoul Frijters, Bart Heupersm pieter van Beek, Maurice Bouwhuis, René van Schaik, Jacob de Vlieg, Jan Polman, and Wynand Alkema, "CoPub: a literature-based keyword enrichment tool for microarray data analysis", Nucleic Acids Research, Vol. 36, No. suppl_2, pp. W406-W410, Apr. 2008. https://doi.org/10.1093/nar/gkn215
  13. Raoul Frijters, Marianne van Vugt, Rubem Smeets, René van Schaik, Jacob de Vlieg, and Wynand Alkema, "Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases", PLoS computational biology, Vol. 6, No. 9, e1000943, Sep. 2010. https://doi.org/10.1371/journal.pcbi.1000943
  14. Wilco W.M. Fleuren and Wynand Alkema, "Application of text mining in the biomedical domain", Methods, Vol. 74, pp. 97-106, Mar. 2015. https://doi.org/10.1016/j.ymeth.2015.01.015
  15. DR Swanson, "Medical literature as a potential source of new knowledge", Bulletin of the Medical Library Association, Vol. 78, No. 1, pp. 29-37, Jan. 1990.
  16. Shin Kim, Jeongwoo Kim, and Sanghyun Park, "Inferring Hidden Drug Effects Using Similarity Drugs", Korea Computer Congress 2015, pp. 2074-2076, Jun. 2015.
  17. Kathi Canese and Sarah Weis, "PubMed: the bibliographic database", The NCBI Handbook [Internet]. 2nd edition., Mar. 2013.
  18. Hyunjin Kim, Youngmi Yoon, Jaegyoon Ahn, and SangHyun Park, "A literature-driven method to calculate similarities among disease", Computer methods and programs in biomedicine, Vol. 122, No. 2, pp. 108-122, Nov. 2015. https://doi.org/10.1016/j.cmpb.2015.07.001
  19. David S. Wishart, Craig Knox, An Ghi Guo, Savita Shrivastava, Murtaza Hassanali, Paul Stothard, Zhan Chang, and Jennifer Woolsey, "DrugBank: a comprehensive resource for in silico drug discovery and exploration", Nucleic Acids Research, Vol. 34, No. suppl_1, pp. D668-D672, Jan. 2006. https://doi.org/10.1093/nar/gkj067
  20. Minoru Kanehisa, Miho Furumichi, Mao Tanabe, Yoko Sato, and Kanae Morishima, "KEGG: new perspectives on genomes, pathways, disease and drugs", Nucleic Acids Research, Vol. 45, No. suppl_D1, pp. D353-D361, Nov. 2016.
  21. Warren A. Kibbe, Cesar Arze, Victor Felix, Elvira Mitraka, Evan Bolton, Gang Fu, Christopher J. Mungall, Janos X. Binder, James Malone, Drashtti Vasant, Helen Parkinson, and Lynn M. Schriml, "Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data", Nucleic Acids Research, Vol. 43, No. D1, pp. D1071-D1078, Jan. 2015. https://doi.org/10.1093/nar/gku1011
  22. M. Whirl-Carrillo, E. M. McDonagh, J. M. Hebert, L. Gong, K. Sangkuhl, C. F. Thorn, R. B. Altman, and T. E. Klein, "Pharmacogenomics knowledge for personalized medicine", Clinical Pharmacology & Therapeutics, Vol. 92, No. 4, pp. 414-417, Oct. 2012. https://doi.org/10.1038/clpt.2012.96
  23. Allan Peter Davis, Cynthia J. Grondin, Kelley Lennon-Hopkins, Cynthia Saraceni-Richards, Daniela Sciaky, Benjamin L. King, Thomas C. Wiegers, and Carolyn J. Mattingly, "The Comparative Toxicogenomics Database's 10th year anniversary: update 2015", Nucleic Acids Research, Vol. 43, No. D1, pp. D914-D920, Jan. 2015. https://doi.org/10.1093/nar/gku935

피인용 문헌

  1. Predicting Disease-related Genes Using Biomedical Literature Based on GloVe Word Embedding vol.18, pp.7, 2020, https://doi.org/10.14801/jkiit.2020.18.7.1
  2. Drug Repositioning through Drug-Disease Bipartite Network vol.18, pp.12, 2018, https://doi.org/10.14801/jkiit.2020.18.12.1