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COVID-19 ile İlgili Genlerin İlişkisel Sınıflandırma Teknikleriyle Değerlendirilmesi

Year 2022, Volume: 14 Issue: 1, 1 - 8, 14.03.2022
https://doi.org/10.18521/ktd.958555

Abstract

Amaç: Bu çalışma, açık erişimli COVID-19 negatif ve pozitif hastalardan oluşan gen veri seti üzerinde ilişkisel sınıflandırma yöntemini uygulayarak COVID-19'u sınıflandırmayı ve COVID-19'a neden olan genleri tanımlayarak bu genlerle hastalık ilişkisini ortaya çıkarmayı amaçlamaktadır.
Gereç ve Yöntem: Bu çalışmada açık erişimli COVID-19 olan ve olmayan hastaların gen veri setine ilişkisel sınıflandırma yöntemi uygulandı. Kullanılan açık erişimli veri setinde 234 kişiye ait 15979 gen bulunmaktadır. 234 kişiden 141'i (%60.3) COVID-19 negatif ve 93'ü (%39.7) COVID-19 pozitifti. Bu çalışmada, ilgili tahmin edici değişkenleri seçmek için değişken seçim yöntemlerinden LASSO gerçekleştirilmiştir. Modelin performansı doğruluk, dengelenmiş doğruluk, duyarlılık, seçicilik, pozitif tahmin değeri, negatif tahmin değeri ve F1 skoru ile değerlendirildi.
Bulgular: Çalışmanın bulgularına göre, ilişkisel sınıflandırma yönteminden performans ölçütleri doğruluk %92.70, dengelenmiş doğruluk %91.80, duyarlılık %87.10, seçicilik %96.50, pozitif tahmin değeri %94.20, negatif tahmin değeri %91.90 ve F1 puanı %90.50 olarak elde edilmiştir.
Sonuç: Önerilen ilişkisel sınıflandırma yöntemi, COVID-19'u sınıflandırmada çok yüksek performans elde etmiştir. Genlerle ilgili çıkarılan birliktelik kuralları, hastalığın teşhis ve tedavisine yardımcı olabilir.

References

  • Dikmen AU, KINA MH, Özkan S, İlhan MN. COVID-19 epidemiyolojisi: Pandemiden ne öğrendik. Journal of biotechnology and strategic health research. 2020;4:29-36.
  • Yücel E, TAMAY ZÜ. Astım ve COVID-19. Çocuk Dergisi.20(2):76-9.
  • KESKİN M, Derya Ö. COVID-19 sürecinde öğrencilerin web tabanlı uzaktan eğitime yönelik geri bildirimlerinin değerlendirilmesi. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi. 2020;5(2):59-67.
  • COVID WA. Outbreak a Pandemic; 2020. Back to cited text.
  • Organization WH. Coronavirus disease (COVID-19) pandemic [cited 2020 14 December]. Available from: https://covid19.who.int/.
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  • Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine. 2020.
  • Mirzaei H, McFarland W, Karamouzian M, Sharifi H. COVID-19 among people living with HIV: a systematic review. AIDS and Behavior. 2020:1-8.
  • Sheikhi K, Shirzadfar H, Sheikhi M. A review on novel coronavirus (Covid-19): symptoms, transmission and diagnosis tests. Research in Infectious Diseases and Tropical Medicine. 2020;2(1):1-8.
  • PALA K. COVID-19 Pandemisi ve Türkiye’de Halk Sağlığı Yönetimi. Sağlık ve Toplum. 2020;30(Özel Sayı):39-50.
  • TANRİVERDİ ES. COVID-19 Etkeninin Özellikleri.
  • Silahtaroğlu G. Kavram ve Algoritmalarıyla Temel Veri Madenciliği Papatya Yayıncılık Eğitim AŞ. İstanbul, Türkiye. 2008.
  • Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record. 2002;31(1):76-7.
  • Moss LT, Atre S. Business intelligence roadmap: the complete project lifecycle for decision-support applications: Addison-Wesley Professional; 2003.
  • Chen Y-L, Chen J-M, Tung C-W. A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decision support systems. 2006;42(3):1503-20.
  • Vinodh S, Prakash NH, Selvan KE. Evaluation of leanness using fuzzy association rules mining. The International Journal of Advanced Manufacturing Technology. 2011;57(1-4):343-52.
  • Thabtah FA. A review of associative classification mining. Knowledge Engineering Review. 2007;22(1):37-65.
  • Mick E, Kamm J, Pisco AO, Ratnasiri K, Babik JM, Calfee CS, et al. Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2. medRxiv. 2020.
  • ÇALIŞAN M, TALU MF. Boyut İndirgeme Yöntemlerinin Karşılaştırmalı Analizi. Türk Doğa ve Fen Dergisi.9(1):107-13.
  • Zou H. The adaptive lasso and its oracle properties. Journal of the American statistical association. 2006;101(476):1418-29.
  • Zhang HH, Lu W. Adaptive Lasso for Cox's proportional hazards model. Biometrika. 2007;94(3):691-703.
  • Kamber M, Pei J. Data mining: Concepts and techniques: Morgan Kaufmann Publishers San Francisco; 2001.
  • Kumar AS, Wahidabanu R, editors. A frequent item graph approach for discovering frequent itemsets. 2008 International Conference on Advanced Computer Theory and Engineering; 2008: IEEE.
  • Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, editors. Advances in knowledge discovery and data mining1996: American Association for Artificial Intelligence.
  • Larose DT, Larose CD. Discovering knowledge in data: an introduction to data mining: John Wiley & Sons; 2014.
  • Han J, Pei J, Kamber M. Data mining: concepts and techniques: Elsevier; 2011.
  • Azmi M, Runger GC, Berrado A. Interpretable regularized class association rules algorithm for classification in a categorical data space. Information Sciences. 2019;483:313-31.
  • ARSLAN AK, Zeynep T, ÇİÇEK İpB, ÇOLAK C. A NOVEL INTERPRETABLE WEB-BASED TOOL ON THE ASSOCIATIVE CLASSIFICATION METHODS: AN APPLICATION ON BREAST CANCER DATASET. The Journal of Cognitive Systems.5(1):33-40.
  • Bingül BA, Türk A, Ak R. Covid-19 Bağlamında Tarihteki Büyük Salgınlar ve Ekonomik Sonuçları. Electronic Turkish Studies. 2020;15(4).
  • Üstün Ç, Özçiftçi S. COVID-19 pandemisinin sosyal yaşam ve etik düzlem üzerine etkileri: Bir değerlendirme çalışması. Anadolu Kliniği Tıp Bilimleri Dergisi. 2020;25(Special Issue on COVID 19):142-53.
  • ÇİFTÇİ E, ÇOKSÜER F. Yeni Koronavirüs İnfeksiyonu: COVID-19. Flora İnfeksiyon Hastalıkları ve Klinik Mikrobiyoloji Dergisi. 2020;25(1):9-18.
  • Murray MF, Kenny EE, Ritchie MD, Rader DJ, Bale AE, Giovanni MA, et al. COVID-19 outcomes and the human genome. Genetics in Medicine. 2020:1-3.
  • Liu B, Hsu W, Ma Y, editors. Integrating classification and association rule mining. KDD; 1998.
  • Jabbar MA, Deekshatulu BL, Chandra P, editors. Heart disease prediction using lazy associative classification. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s); 2013: IEEE.
  • Haafeeza K, Mohanraj R. Classification of Multi Disease Diagnosing and Treatement Analysis Based on Hybrid Mining Technique. International Journal of Advanced Technology and Innovative Research. 2014;6(3):108-16.

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

Year 2022, Volume: 14 Issue: 1, 1 - 8, 14.03.2022
https://doi.org/10.18521/ktd.958555

Abstract

Objective: This study aims to classify COVID-19 by applying the associative classification method on the gene data set consisting of open access COVID-19 negative and positive patients and revealing the disease relationship with these genes by identifying the genes that cause COVID-19.
Method: In the study, an associative classification model was applied to the gene data set of patients with and without open access COVID-19. In this open-access data set used, 15979 genes are belonging to 234 individuals. Out of 234 people, 141 (60.3%) were COVID-19 negative and 93 (39.7%) were COVID-19 positives. In this study, LASSO, one of the feature selection methods, was performed to choose the relevant predictors. The models' performance was evaluated with accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.
Results: According to the study findings, the performance metrics from the associative classification model were accuracy of 92.70%, balanced accuracy of 91.80%, the sensitivity of 87.10%, the specificity of 96.50%, the positive predictive value of 94.20%, the negative predictive value of 91.90%, and F1-score of 90.50%.
Conclusion: The proposed associative classification model achieved very high performances in classifying COVID-19. The extracted association rules related to the genes can help diagnose and treat the disease.

References

  • Dikmen AU, KINA MH, Özkan S, İlhan MN. COVID-19 epidemiyolojisi: Pandemiden ne öğrendik. Journal of biotechnology and strategic health research. 2020;4:29-36.
  • Yücel E, TAMAY ZÜ. Astım ve COVID-19. Çocuk Dergisi.20(2):76-9.
  • KESKİN M, Derya Ö. COVID-19 sürecinde öğrencilerin web tabanlı uzaktan eğitime yönelik geri bildirimlerinin değerlendirilmesi. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi. 2020;5(2):59-67.
  • COVID WA. Outbreak a Pandemic; 2020. Back to cited text.
  • Organization WH. Coronavirus disease (COVID-19) pandemic [cited 2020 14 December]. Available from: https://covid19.who.int/.
  • AYTÜR YK, KÖSEOĞLU B, TAŞKIRAN ÖÖ, GÖKKAYA NKO, DELİALİOĞLU SÜ, TUR BS, et al. SARS-CoV-2 (COVID-19) sonrası pulmoner rehabilitasyon prensipleri: Akut ve subakut sürecin yönetimi için rehber. Fiziksel Tıp ve Rehabilitasyon Bilimleri Dergisi. 2020;23(2):111-23.
  • Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine. 2020.
  • Mirzaei H, McFarland W, Karamouzian M, Sharifi H. COVID-19 among people living with HIV: a systematic review. AIDS and Behavior. 2020:1-8.
  • Sheikhi K, Shirzadfar H, Sheikhi M. A review on novel coronavirus (Covid-19): symptoms, transmission and diagnosis tests. Research in Infectious Diseases and Tropical Medicine. 2020;2(1):1-8.
  • PALA K. COVID-19 Pandemisi ve Türkiye’de Halk Sağlığı Yönetimi. Sağlık ve Toplum. 2020;30(Özel Sayı):39-50.
  • TANRİVERDİ ES. COVID-19 Etkeninin Özellikleri.
  • Silahtaroğlu G. Kavram ve Algoritmalarıyla Temel Veri Madenciliği Papatya Yayıncılık Eğitim AŞ. İstanbul, Türkiye. 2008.
  • Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record. 2002;31(1):76-7.
  • Moss LT, Atre S. Business intelligence roadmap: the complete project lifecycle for decision-support applications: Addison-Wesley Professional; 2003.
  • Chen Y-L, Chen J-M, Tung C-W. A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decision support systems. 2006;42(3):1503-20.
  • Vinodh S, Prakash NH, Selvan KE. Evaluation of leanness using fuzzy association rules mining. The International Journal of Advanced Manufacturing Technology. 2011;57(1-4):343-52.
  • Thabtah FA. A review of associative classification mining. Knowledge Engineering Review. 2007;22(1):37-65.
  • Mick E, Kamm J, Pisco AO, Ratnasiri K, Babik JM, Calfee CS, et al. Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2. medRxiv. 2020.
  • ÇALIŞAN M, TALU MF. Boyut İndirgeme Yöntemlerinin Karşılaştırmalı Analizi. Türk Doğa ve Fen Dergisi.9(1):107-13.
  • Zou H. The adaptive lasso and its oracle properties. Journal of the American statistical association. 2006;101(476):1418-29.
  • Zhang HH, Lu W. Adaptive Lasso for Cox's proportional hazards model. Biometrika. 2007;94(3):691-703.
  • Kamber M, Pei J. Data mining: Concepts and techniques: Morgan Kaufmann Publishers San Francisco; 2001.
  • Kumar AS, Wahidabanu R, editors. A frequent item graph approach for discovering frequent itemsets. 2008 International Conference on Advanced Computer Theory and Engineering; 2008: IEEE.
  • Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, editors. Advances in knowledge discovery and data mining1996: American Association for Artificial Intelligence.
  • Larose DT, Larose CD. Discovering knowledge in data: an introduction to data mining: John Wiley & Sons; 2014.
  • Han J, Pei J, Kamber M. Data mining: concepts and techniques: Elsevier; 2011.
  • Azmi M, Runger GC, Berrado A. Interpretable regularized class association rules algorithm for classification in a categorical data space. Information Sciences. 2019;483:313-31.
  • ARSLAN AK, Zeynep T, ÇİÇEK İpB, ÇOLAK C. A NOVEL INTERPRETABLE WEB-BASED TOOL ON THE ASSOCIATIVE CLASSIFICATION METHODS: AN APPLICATION ON BREAST CANCER DATASET. The Journal of Cognitive Systems.5(1):33-40.
  • Bingül BA, Türk A, Ak R. Covid-19 Bağlamında Tarihteki Büyük Salgınlar ve Ekonomik Sonuçları. Electronic Turkish Studies. 2020;15(4).
  • Üstün Ç, Özçiftçi S. COVID-19 pandemisinin sosyal yaşam ve etik düzlem üzerine etkileri: Bir değerlendirme çalışması. Anadolu Kliniği Tıp Bilimleri Dergisi. 2020;25(Special Issue on COVID 19):142-53.
  • ÇİFTÇİ E, ÇOKSÜER F. Yeni Koronavirüs İnfeksiyonu: COVID-19. Flora İnfeksiyon Hastalıkları ve Klinik Mikrobiyoloji Dergisi. 2020;25(1):9-18.
  • Murray MF, Kenny EE, Ritchie MD, Rader DJ, Bale AE, Giovanni MA, et al. COVID-19 outcomes and the human genome. Genetics in Medicine. 2020:1-3.
  • Liu B, Hsu W, Ma Y, editors. Integrating classification and association rule mining. KDD; 1998.
  • Jabbar MA, Deekshatulu BL, Chandra P, editors. Heart disease prediction using lazy associative classification. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s); 2013: IEEE.
  • Haafeeza K, Mohanraj R. Classification of Multi Disease Diagnosing and Treatement Analysis Based on Hybrid Mining Technique. International Journal of Advanced Technology and Innovative Research. 2014;6(3):108-16.
There are 35 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Articles
Authors

İpek Balıkçı Çiçek 0000-0002-3805-9214

Dr. Öğr. Üyesi Mehmet Onur Kaya 0000-0001-8052-0484

Cemil Çolak 0000-0001-8052-0484

Publication Date March 14, 2022
Acceptance Date November 4, 2021
Published in Issue Year 2022 Volume: 14 Issue: 1

Cite

APA Balıkçı Çiçek, İ., Kaya, D. Ö. Ü. M. O., & Çolak, C. (2022). Assessment of COVID-19-Related Genes Through Associative Classification Techniques. Konuralp Medical Journal, 14(1), 1-8. https://doi.org/10.18521/ktd.958555
AMA Balıkçı Çiçek İ, Kaya DÖÜMO, Çolak C. Assessment of COVID-19-Related Genes Through Associative Classification Techniques. Konuralp Medical Journal. March 2022;14(1):1-8. doi:10.18521/ktd.958555
Chicago Balıkçı Çiçek, İpek, Dr. Öğr. Üyesi Mehmet Onur Kaya, and Cemil Çolak. “Assessment of COVID-19-Related Genes Through Associative Classification Techniques”. Konuralp Medical Journal 14, no. 1 (March 2022): 1-8. https://doi.org/10.18521/ktd.958555.
EndNote Balıkçı Çiçek İ, Kaya DÖÜMO, Çolak C (March 1, 2022) Assessment of COVID-19-Related Genes Through Associative Classification Techniques. Konuralp Medical Journal 14 1 1–8.
IEEE İ. Balıkçı Çiçek, D. Ö. Ü. M. O. Kaya, and C. Çolak, “Assessment of COVID-19-Related Genes Through Associative Classification Techniques”, Konuralp Medical Journal, vol. 14, no. 1, pp. 1–8, 2022, doi: 10.18521/ktd.958555.
ISNAD Balıkçı Çiçek, İpek et al. “Assessment of COVID-19-Related Genes Through Associative Classification Techniques”. Konuralp Medical Journal 14/1 (March 2022), 1-8. https://doi.org/10.18521/ktd.958555.
JAMA Balıkçı Çiçek İ, Kaya DÖÜMO, Çolak C. Assessment of COVID-19-Related Genes Through Associative Classification Techniques. Konuralp Medical Journal. 2022;14:1–8.
MLA Balıkçı Çiçek, İpek et al. “Assessment of COVID-19-Related Genes Through Associative Classification Techniques”. Konuralp Medical Journal, vol. 14, no. 1, 2022, pp. 1-8, doi:10.18521/ktd.958555.
Vancouver Balıkçı Çiçek İ, Kaya DÖÜMO, Çolak C. Assessment of COVID-19-Related Genes Through Associative Classification Techniques. Konuralp Medical Journal. 2022;14(1):1-8.