정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제8권2호
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  • Pages.51-60
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  • 2019
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  • 2287-5905(pISSN)
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  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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유전자 질병 관련도 분석을 위한 소프트웨어 플랫폼

Software Platform for Analyzing Gene and Disease Relevance

  • 투고 : 2018.07.13
  • 심사 : 2018.10.01
  • 발행 : 2019.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

많은 질병들이 정복되면서 삶의 질을 향상시키지만, 유전병들은 많은 분석 및 연구가 필요하다. 이러한 질병과 유전자 관련도를 분석할 때 다양한 요구사항이 존재하고, 알고리즘 최적화 유무로 인해 런타임 효율성이 저하된다. 본 논문은 유전자 질병 관련도 분석 플랫폼을 소개하고 위의 이슈를 해결하기 위한 분석 API와 두가지 런타임 효율성 최적화 알고리즘을 제시한다. 그리고 제시한 분석 API를 이용하여 관련도 측정 실험을 진행, 두 최적화 알고리즘의 결과와 비교했다. 첫 번째 알고리즘은 이전 실험과 같은 결과를 적은 시간에 도출했고, 두 번째 알고리즘은 이전 실험들에 비해 낮은 정확도의 결과를 더 적은 시간에 도출했다. 따라서 본 플랫폼을 통해 여러 방식의 유전자와 질병 관련도를 효율적으로 얻을 수 있다.

While the quality of life is enhanced as many types of diseases are remedied, there is a high demand for analysis and research on gene-related diseases. There exists various forms and requirements in analyzing the relevance between genes and diseases, and the runtime efficiency can be decreased due to the level of algorithm optimization. This paper proposes a platform for analyzing gene disease relevance, provides API for remedying the variability issue, and suggests two algorithms which optimize the runtime efficiency. And, we conduct experiments for measuring the relevancy using the analysis API, and compare the two algorithms. The first algorithm is to improve the runtime efficiency comparing to the conventional methods, and the second algorithm is to improve the runtime efficiency with lower accuracy. This platform can be well utilized for analyzing various forms of gene disease analytics.

키워드

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Fig. 1. Gene, Disease and Relation Object Structure

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Fig. 2. Gene Disease Relation Extension Graph

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Fig. 3. A Part of Relation Data

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Fig. 4. Comparing findRelatedGenes_SD Methods

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Fig. 5. Comparing findRelatedGenes_MD Methods

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Fig. 6. Comparing findRelatedDiseases_SG Methods

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Fig. 7. First Method Elapsed Time

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Fig. 8. Comparing Approximated Algorithm Elapsed Time with Optimized Algorithm

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Fig. 9. Comparing Approximated Algorithm Accuracy with Optimized Algorithm

Table 1. The Platform Sectors and Methods

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Table 2. Finding Genes for given Diseases Methods

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Table 3. Finding Diseases for given Genes Methods

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Table 4. A Disease and Gene Relation Algorithm

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Table 5. A Gene and Disease Relation Algorithm

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Table 6. Gene Set and Disease Relation Algorithm

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Table 7. Gene Set and Relation Algorithm

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Table 8. Relation Measurement Algorithm

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Table 9. Dynamic Programming Based Algorithm

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Table 10. Approximation Based Algorithm

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Table 11. findRelatedGenes_SD() Method Result

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Table 12. findRelatedGenes_MD() Method Result

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Table 13. findRelatedDiseases_SG() Method Result

JBCRJM_2019_v8n2_51_t0013.png 이미지

참고문헌

  1. Man-Sun Kim and Jeong-Rae Kim, "Characterization of the Alzheimer's disease-related network based on the dynamic network approach," Journal of Korean Institute of Intelligent Systems, Vol.26, No.6, pp.529-535, Dec. 2015.
  2. Kai Zheng, Gangquan Si, and Chen Duan., "Diagnosis of kashin-beck disease and other common joint diseases via a gene model," in Proceedings of IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017), Chengdu, China, Dec. pp.808-812, 2017.
  3. Gui-Qiong Zhu and Pei-Hui Yang, "Identifying the candidate genes for Alzheimer's disease based on the rejection region of T test," International Conference on Machine Learning and Cybernetics (ICMLC 2016), Vol.2, pp.732-736, 2016.
  4. Rohit Gupta, S. M. Fayaz, and Sanjay Singh, "Identification of gene network motifs for cancer disease diagnosis," in Proceedings of IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER 2016), India, pp.179-184, Aug. 2016.
  5. Jahnabi Dutta, Surama Biswas, and Souvik Saha, "Identification of disease-critical genes causing preeclampsia: Meta-heuristic approaches" in Proceedings of IEEE UP Section Conference on Electrical Computer and Electronics (UPCON 2015), Allahabad, India, pp.1-6, Dec. 2015.
  6. Ji-Hwan Ha, Hyun-Jin Kim, and Sang-Hyun Park, "An Extraction Method of Disease-Related miRNA through the Target Gene Based miRNA Network," Journal of Korean Institute of Information Technology, Vol.13 No.1, pp.113-119, Jan. 2015. https://doi.org/10.14801/jkiit.2015.13.1.113
  7. Jong-Keun Lee, S.S Park, D.W. Hong, et al, "Development of a Gene's Functional Classifying System for a Microarray Data using a Gene Ontology," Journal of Korea Information Science Society, Vol.33, No.2C, pp.246-251, Oct. 2006.
  8. Xiwei Tang, Xiaohua Hu, Xuejun Yang, et al., "A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks," in Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), Shenzhen, China, Dec. pp.308-311, 2016.
  9. Shadi A. Aljawarneh, Reem Jaradat, Abdelsalam M. Maatuk, et al., "Gene profile classification: A proposed solution for predicting possible diseases and initial results," in Proceedings of International Conference on Engineering & MIS (ICEMIS 2016), Agadir, Morocco, pp. 1-7, Sep. 2016.
  10. Shadi A. Aljawarneh, Reem Jarada, et al., "Gene profile classification A proposed solution for predicting possible diseases and initial results," in Proceedings of IEEE Engineering & MIS (ICEMIS 2016), Agadir, Morocco, pp.1-7, Nov. 2016.
  11. Amirhossein Tavanaei, Nishanth Anandanadarajah, et al., "A deep learning model for predicting tumor suppressor genes and oncogenes from PDB structure," in Proceedings of Bioinformatics and Biomedicine (BIBM 2017), MO, USA, pp.613-617, Dec. 2017.
  12. TengJiao Wang, Wei Liu HaiLin, et al., "Predicting potential disease-related genes using the network topological features," in Proceedings of Human Health and Biomedical Engineering (HHBE 2011), Jilin, China, pp.871-876, Sep. 2011.
  13. Mustafa Ozgur Cingiz, and Banu Diri, "The inference of gene co-expression networks of breast and colon cancer using miRNA-target gene interactions data," in Proceedings of Signal Processing and Communications Applications Conference (SIU 2017), Antalya, Turkey, pp.1-4, Jun. 2017.
  14. Ashkan Entezari Heravi and Sheridan Houghten, "A methodology for disease gene association using centrality measures," in Proceedings of IEEE Congress on Evolutionary Computation (CEC 2016), Vancouver, Canada, pp. 24-31, Jul. 2016.
  15. Ashwani Kumar and Tiratha Raj Singh, "Systems biology approach for gene set enrichment and topological analysis of Alzheimer's disease pathway," in Proceedings of International Conference on Bioinformatics and Systems Biology (BSB 2016), Allahabad, India, pp.1-5, Mar. 2016.
  16. Nivit Grewal, Shailendra Singh, and Trilok Chand, "Effect of Aggregation Operators on Network-Based Disease Gene Prioritization: A Case Study on Blood Disorders", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.14, No.6, pp.1276-1287, Aug. 2016. https://doi.org/10.1109/TCBB.2016.2599155
  17. Kazutaka Nishiwaki, Katsutoshi Kanamori, et al., "Finding a disease-related gene from microarray data using random forest," in Proceedings of Cognitive Informatics & Cognitive Computing (ICCI*CC 2017), CA, USA, pp.542-546, Feb. 2017.
  18. Khalid M.O. Nahar and Izzat Alsmadi, "Information analysis to study interactions between different genes and diseases," in Proceedings of 8th International Conference on Information Technology (ICIT 2017), Bhubaneswar, India, pp.1-5, Dec. 2017.
  19. Zhen Tian, Maozu Guo, Chunyu Wang, et al., "Constructing an integrated gene similarity network for the identification of disease genes," in Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), Shenzhen, China, pp.1663-1668, Dec. 2016.
  20. Kyungsik Ha, Jinmuk Lim, and Hong-Gee Kim, "Integrated Model design of miRNA, PPI and Disease Information," Journal of Korea Information Science Society, Vol.39, No.1B, pp.492-494, Jun. 2012.
  21. Kyungsik Ha, Jinmuk Lim, and Hong-Gee Kim, "Integrated Model Design of Microarray Data Using miRNA, PPI, Disease Information," Journal of Korean Institute of Intelligent Systems, Vol.22, No.1B, pp.492-494, Jun. 2012.
  22. Jeongwoo Kim and Sanghyun Park, "ISN: Inferring disease-related genes using seed gene and network analysis," in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017), Banff, Canada, pp.2012-2017, Oct. 2017.
  23. Sun-Wook Choi and Chong Ho Lee, "Gene Expression Data Analysis Using Parallel Processor based Pattern Classification Method," Journal of The Institute of Electronics Engineers of Korea, Vol.46, No.6, pp.44-55, Nov. 2009.
  24. Kai Zheng, Gangquan Si, and Chen Duan., "Application of a gene diagnosis model in the identification of Kashin-Beck disease and similar joint diseases," in Proceedings of IEEE International Conference on Computer and Communications (ICCC 2017), Chengdu, China, pp.2669-2673, Dec. 2017.
  25. Pradipta Maji and Ekta Shah, "Significance and Functional Similarity for Identification of Disease Genes," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.14, No.16, pp.1419-1433, Aug. 2016.
  26. Hyung-Seok Choi, "Systematic Integrative Analysis of Heterogeneous Bio Big Data for Identification of Diseasecausing Gene Network," Journal of Korea Information Science Society, Vol.31, No.5, pp.61-68, Aug. 2013.
  27. Mengmeng Wu, Wanwen Zeng, Wenqiang Liu, et al., "Integrating embeddings of multiple gene networks to prioritize complex disease-associated genes," in Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), MO, USA, pp.208-215, Nov. 2017.
  28. Sun-Wook Choi and Chong Ho Lee, "Disease Classfication using Random Subspace Method based on Gene Interaction Information and mRMR Filter," Journal of Korean Institute of Intelligent Systems, Vol.22, No.2, pp.192-197, Apr. 2012. https://doi.org/10.5391/JKIIS.2012.22.2.192
  29. Chae-Gyun Lim, A.K.M. Tauhidul Islam, and Byeong-Soo Jeong, "Efficient Parallel Gene Selection Method based on MapReduce," Journal of Korea Information Science Society: Databases, Vol.41, No.3, pp.162-167, Jun. 2014.