Прогнозирование особенностей течения хронического гепатита C с использованием байесовских сетей
Прогнозирование особенностей течения хронического гепатита C с использованием байесовских сетей
Самоходская Л.М., Старостина Е.Е., Сулимов А.В. и др. Прогнозирование особенностей течения хронического гепатита C с использованием байесовских сетей. Терапевтический архив. 2019; 91 (2): 8–39. DOI: 10.26442/00403660.2019.02.000076
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Аннотация
Материалы и методы. В исследование включено 253 больных хроническим гепатитом С (ХГС) и циррозом печени (ЦП), у которых исследовались точечные мутации генов, участвующих в воспалительных реакциях и противовирусном иммунитете (IL-1β -511C/T, IL-10 -1082G/A, IL28B C/T, IL28B T/G, TNF-α -238G/A, TGF-β -915G/C, IL-6 -174G/C), активаторов локального печеночного фиброза (AGT G-6A, AGT 235 M/T, ATR1 1166 A/C), гемохроматоза (HFE C282Y, HFE H63D), тромбоцитарных рецепторов (ITGA2 807 C/T, ITGB3 1565 T/C), белков свертывающей системы и эндотелиальной дисфункции (FII 20210 G/A, FV 1691G/A, FVII 10976 G/A, FXIII 103 G/T, eNOS 894 G/T, CYBA 242 C/T, FBG -455 G/A, PAI -675 5G/4G, MTHFR 677 C/T). С использованием байесовских сетей (БС) изучалось предикторное значение клинико-лабораторных факторов для следующих состояний – конечных точек (КТ): развитие цирроза печени (КТ1), скорость фиброза (КТ2), наличие портальной гипертензии (КТ3) и наличие криоглобулинов (КТ4).
Результаты и обсуждение. Кроме традиционных факторов нами был показан вклад следующих мутаций. При прогнозировании КТ1 – ЦП – значимыми оказались наличие мутации гена HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C. Нами также выявлена связь между темпом прогрессирования фиброза печени и наличием полиморфизма генов AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Среди генетических факторов, связанных с портальной гипертензией, оказались полиморфизм генов PAI-I -675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D и Il-6 174GC. Наличие криоглобулинов и криоглобулиемический васкулит (КТ4) ассоциировано с мутациями в гене MTHFR C677T, генах ATR A1166C и HFE H63D.
Заключение. Полученные результаты позволили уточнить вклад основных патофизиологических и генетических факторов, определяющих статус больного и исход заболевания, а также выявить значимость точечных мутаций генов, контролирующих основные пути формирования и прогрессирования ХГС.
Ключевые слова: хронический гепатит С, полиморфизм генов, тромбофилия, тромбоцитарные рецепторы, байесовские сети.
Results and discussion. In addition to traditional factors we have shown the contribution of the following mutations. Predicting EP1- liver cirrhosis – HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C gene mutations were significant. We also found a link between the rate of progression of liver fibrosis and gene polymorphisms of AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Among the genetic factors associated with portal hypertension there are gene polymorphisms of PAI-I-675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D and Il-6 174GC. Cryoglobulins and cryoglobuliemic vasculitis (EP4) are associated with gene mutations MTHFR C677T, ATR A1166C and HFE H63D.
Conclusion. The results obtained allow to detect the major pathophysiological and genetic factors which determine the status of the patient and the outcome of the disease, to clarify their contribution, and to reveal the significance of point mutations of genes that control the main routes of HCV course and progression.
Keywords: chronic hepatitis C, gene polymorphism, thrombophilia, platelet receptors, Bayesian networks.
Результаты и обсуждение. Кроме традиционных факторов нами был показан вклад следующих мутаций. При прогнозировании КТ1 – ЦП – значимыми оказались наличие мутации гена HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C. Нами также выявлена связь между темпом прогрессирования фиброза печени и наличием полиморфизма генов AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Среди генетических факторов, связанных с портальной гипертензией, оказались полиморфизм генов PAI-I -675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D и Il-6 174GC. Наличие криоглобулинов и криоглобулиемический васкулит (КТ4) ассоциировано с мутациями в гене MTHFR C677T, генах ATR A1166C и HFE H63D.
Заключение. Полученные результаты позволили уточнить вклад основных патофизиологических и генетических факторов, определяющих статус больного и исход заболевания, а также выявить значимость точечных мутаций генов, контролирующих основные пути формирования и прогрессирования ХГС.
Ключевые слова: хронический гепатит С, полиморфизм генов, тромбофилия, тромбоцитарные рецепторы, байесовские сети.
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Results and discussion. In addition to traditional factors we have shown the contribution of the following mutations. Predicting EP1- liver cirrhosis – HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C gene mutations were significant. We also found a link between the rate of progression of liver fibrosis and gene polymorphisms of AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Among the genetic factors associated with portal hypertension there are gene polymorphisms of PAI-I-675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D and Il-6 174GC. Cryoglobulins and cryoglobuliemic vasculitis (EP4) are associated with gene mutations MTHFR C677T, ATR A1166C and HFE H63D.
Conclusion. The results obtained allow to detect the major pathophysiological and genetic factors which determine the status of the patient and the outcome of the disease, to clarify their contribution, and to reveal the significance of point mutations of genes that control the main routes of HCV course and progression.
Keywords: chronic hepatitis C, gene polymorphism, thrombophilia, platelet receptors, Bayesian networks.
Список литературы
1. Самоходская Л.М., Старостина Е.Е., Яровая Е.Б., Краснова Т.Н., Мухин Н.А., Ткачук В.А., Садовничий В.А. Математическая модель прогноза скорости фиброза печени у больных с хроническим гепатитом С на основе комбинаций геномных маркеров. Вестник Российской академии медицинских наук. 2015;70(5):651-61 [Samokhodskaya LM, Starostina EE, Yarovaya EB, Krasnova TM, Mukhin NA, Tkachuk VA, Sadovnichiy VA. Mathematic model of prognosis of the liver fibrosis progression rate in patients with chronic hepatitis C based on the combination of genomic markers. Vestnik Rossiyskoy Akademii Medicinskikh Nauk. 2015;70(5):651-61 (In Russ.)].
2. Сулимов А.В., Втюрина Д.Н., Романов А.Н., Масленников Е.Д., Сулимов В.Б., Курочкин И.Н., Упоров И.В., Затейщиков Д.А., Носиков В.В., Варфоломеев С.Д. Экспертные системы персонифицированной медицины: применение байесовских сетей для предсказания состояния пациентов. В кн.: Пост-геномные исследования и технологии (под ред. чл.-корр. РАН С.Д. Варфоломеева). Москва: Изд-во МГУ; 2011. С. 641-702 [Sulimov AV, Vtyurina DN, Romanov AN, Maslennikov ED, Sulimov VB, Kurochkin IN, Uporov IV, Zateystchikov DA, Nosikov VV, Varfolomeev SD. Expert systems of personalized medicine: using Bayesian networks for prediction of patients condition. In: Post-genomnye issledovaniya i tekhnologii [Post-genomic investigations and technologies] (edited by a corresponding member of the Russian Academy of Sciences SD Varfolomeev). Moscow: MSU publishing house; 2011. P. 641-702 (In Russ.)].
3. Генс Г.П., Сулимов А.В., Моисеева Н.И., Каткова Е.В., Вельшер Л.З., Коробкова Л.И., Савкин И.А., Сулимов В.Б. Применение генных сигнатур и медицинских экспертных систем для прогнозирования клинических исходов рака молочной железы. Вестник РОНЦ им. Н.Н. Блохина РАМН. 2015;26(4):16-33 [Gens GP, Sulimov AV, Moiseeva NI, Katkova EV, Velsher LZ, Korobkova LI, Savkin IA, Sulimov VB. Application of gene signatures and medical expert systems for prediction of the clinical outcomes of breast cancer. Vestnic RONC im. N.N. Blochina RAMN. 2015;26(4):16-33 (In Russ.)].
4. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian network sinbiomedicine andhealth-care. Artif Intell Med. 2004;30(3):201-14. doi: 10.1016/j.artmed.2003.11.001
5. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006;22(14):184-90. doi: 10.1093/bioinformatics/btl230
6. Poynard T, Ratziu V, Charlotte F, Goodman Z, Mchutchison J, Albrecht J. Rates and risk factors of liver fibrosis progression in patients with chronic hepatitis C. J Hepatol. 2001;34(5):730-9.
7. Jensen FV, Nielsen TD. Bayesian Networks and Decision Graphs. New York: Springer Verlag; 2007: 193 p.
8. Obuchowski NA. ROC analysis. Am J Roentgenol. 2005;184(2):364-72. doi: 10.2214/ajr.184.2.01840364
9. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi: 10.1148/radiology.143.1.7063747
10. Maslennikov ED, Sulimov AV, Savkin IA, Evdokimova MA, Zateyshchikov DA, Nosikov VV, Sulimov VB. An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine. J Applied Statistics. 2015;42(1):71-87. doi: 10.1080/02664763.2014.934664
11. Генс Г.П., Сулимов А.В., Моисеева Н.И., Овсий О.Г., Вельшер Л.З., Рыбалкина Е.Ю., Селезнева И.И., Савкин И.А., Сулимов В.Б. Поиск подходов к прогнозированию исходов рака молочной железы с помощью байесовских сетей. Онкология. 2014;3(5):37-46 [Gens GP, Sulimov AV, Moiseeva NI, Ovsiy OG, Velsher LZ, Rybalkina EYu, Seleznyova II, Savkin IA, Sulimov VB. Search for approaches for prediction of breast cancer using Bayesian networks. Onkologiya. 2014;3(5):37-46 (In Russ.)].
12. Топтыгина А.П., Азиатцева В.В., Савкин И.А., Кислицин А.А., Семикина Е.Л., Гребенников Д.С., Алешкин А.В., Сулимов А.В., Сулимов В.Б., Бочаров Г.А. Прогнозирование специфического гуморального иммунного ответа на основании исходных параметров иммунного статуса детей, привитых против кори, краснухи и эпидемического паротита. Иммунология. 2015;36(1):22-30 [Toptygina AP, Aziatceva VV, Savkin IA, Kislitsyn AA, Semikina EL, Grebennikov DS, Aleshkin AV, Sulimov AV, Sulimov VB. Prediction of a specific humoral immune response based on the initial parameters of the immune status of children vaccinated against measles, rubella and mumps. Immunologiya. 2015;36(1):22-30 (In Russ.)].
13. Sulimov AV, Meshkov AN, Savkin IA, Katkova EV, Kutov DC, Hasanova ZB, Konovalova NV, Kukharchuk VV, Sulimov VB. Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with bayesian networks. J Comp Eng Math. 2015;2(4):11-26. doi: 10.14529/jcem150402
14. Vanagas G. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact Cardiovasc Thorac Surg. 2004;3(2):319-22. doi: 10.1016/j.icvts.2004.01.008
15. Tung BY, Emond MJ, Bronner MP, Raaka SD, Cotler SJ, Kowdley KV. Hepatitis C, iron status, and disease severity: relationship with HFE mutations. Gastroenterology. 2003;124(2):318-26. doi: 10.1053/gast.2003.50046
16. Barbaro G, Di Lorenzo G, Ribersani M, Soldini M, Giancaspro G, Bellomo G, Belloni G, Grisorio B, Barbarini G. Serum ferritin and hepatic glutathione concentrations in chronic hepatitis C patients related to the hepatitis C virus genotype. J Hepatol. 1999;30(5):774-82.
17. Erhardt A, Maschner-Olberg A, Mellenthin C, Kappert G, Adams O, Donner A, Willers R, Niederau C, Haussinger D. HFE mutations and chronic hepatitis C: H63D and C282Y heterozygosity are independent risk factors for liver fibrosis and cirrhosis. J Hepatol. 2003;38(3):335-42.
18. Moreno MU, José GS, Fortuño A, Beloqui O, Díez J, Zalba G. The C242T CYBA polymorphism of NADPH oxidase is associated with essential hypertension. J Hypertens. 2006;24(7):1299-306. doi: 10.1097/01.hjh.0000234110.54110.56
19. Altarescu G, Haim S, Elstein D. Angiotensinogen promoter and angiotensinogen II receptor type 1 gene polymorphisms and incidence of ischemic stroke and neurologic phenotype in Fabry disease. Biomarkers. 2013;18(7):595-600. doi: 10.3109/1354750X.2013.836244
20. Anstee QM, Dhar A, Thursz MR. The role of hypercoagulability in liver fibrogenesis. Clin Res Hepatol Gastroenterol. 2011;35(8-9):526-33. doi: 10.1016/j.clinre.2011.03.011
21. Plompen EP, Darwish Murad S, Hansen BE, Loth DW, Schouten JN, Taimr P, Hofman A, Uitterlinden AG, Stricker BH, Janssen HL, Leebeek FW. Prothrombotic Genetic Risk Factors are associated with an Increased Risk of Liver Fibrosis in the General Population: The Rotterdam Study. J Hepatol. 2015;63(6):1459-65. doi: 10.1016/j.jhep.2015.07.026
22. Wright M, Goldin R, Hellier S, Knapp S, Frodsham A, Hennig B, Hill A, Apple R, Cheng S, Thomas H, Thursz M. Factor V Leiden polymorphism and the rate of fibrosis development in chronic hepatitis C virus infection. Gut. 2003;52(8):1206-10.
23. Bochud PY, Cai T, Overbeck K, Bochud M, Dufour JF, Mullhaupt B, Borovicka J, Heim M, Moradpour D, Cerny A, Malinverni R, Francioli P, Negro F. Genotype 3 is associated with accelerated fibrosis progression in chronic hepatitis C. J Hepatol. 2009;51(4):655-66. doi: 10.1016/j.jhep.2009.05.016
24. Dammacco F, Sansonno D. Therapy for hepatitis C virus-related cryoglobulinemic vasculitis. N Engl J Med. 2013;369(11):1035-45. doi: 10.1056/NEJMra1208642
25. Saadoun D, Asselah T, Resche-Rigon M, Charlotte F, Bedossa P, Valla D, Piette JC, Marcellin P, Cacoub P. Cryoglobulinemia is associated with steatosis and fibrosis in chronic hepatitis C. Hepatology. 2006;43:1337-45. doi: 10.1002/hep.21190
26. Casato M, Carlesimo M, Francia A, Timarco C, Antenucci A, Bove M, Martini H, Visentini M, Fiorilli M, Conti L. Influence of inherited and acquired thrombophilic defects on the clinical manifestations of mixed cryoglobulinaemia. Rheumatology (Oxford). 2008;47:1659-63. doi: 10.1093/rheumatology/ken303
2. Sulimov AV, Vtyurina DN, Romanov AN, Maslennikov ED, Sulimov VB, Kurochkin IN, Uporov IV, Zateystchikov DA, Nosikov VV, Varfolomeev SD. Expert systems of personalized medicine: using Bayesian networks for prediction of patients condition. In: Post-genomnye issledovaniya i tekhnologii [Post-genomic investigations and technologies] (edited by a corresponding member of the Russian Academy of Sciences SD Varfolomeev). Moscow: MSU publishing house; 2011. P. 641-702 (In Russ.)
3. Gens GP, Sulimov AV, Moiseeva NI, Katkova EV, Velsher LZ, Korobkova LI, Savkin IA, Sulimov VB. Application of gene signatures and medical expert systems for prediction of the clinical outcomes of breast cancer. Vestnic RONC im. N.N. Blochina RAMN. 2015;26(4):16-33 (In Russ.)
4. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian network sinbiomedicine andhealth-care. Artif Intell Med. 2004;30(3):201-14. doi: 10.1016/j.artmed.2003.11.001
5. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006;22(14):184-90. doi: 10.1093/bioinformatics/btl230
6. Poynard T, Ratziu V, Charlotte F, Goodman Z, Mchutchison J, Albrecht J. Rates and risk factors of liver fibrosis progression in patients with chronic hepatitis C. J Hepatol. 2001;34(5):730-9.
7. Jensen FV, Nielsen TD. Bayesian Networks and Decision Graphs. New York: Springer Verlag; 2007: 193 p.
8. Obuchowski NA. ROC analysis. Am J Roentgenol. 2005;184(2):364-72. doi: 10.2214/ajr.184.2.01840364
9. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi: 10.1148/radiology.143.1.7063747
10. Maslennikov ED, Sulimov AV, Savkin IA, Evdokimova MA, Zateyshchikov DA, Nosikov VV, Sulimov VB. An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine. J Applied Statistics. 2015;42(1):71-87. doi: 10.1080/02664763.2014.934664
11. Gens GP, Sulimov AV, Moiseeva NI, Ovsiy OG, Velsher LZ, Rybalkina EYu, Seleznyova II, Savkin IA, Sulimov VB. Search for approaches for prediction of breast cancer using Bayesian networks. Onkologiya. 2014;3(5):37-46 (In Russ.)
12. Toptygina AP, Aziatceva VV, Savkin IA, Kislitsyn AA, Semikina EL, Grebennikov DS, Aleshkin AV, Sulimov AV, Sulimov VB. Prediction of a specific humoral immune response based on the initial parameters of the immune status of children vaccinated against measles, rubella and mumps. Immunologiya. 2015;36(1):22-30 (In Russ.)
13. Sulimov AV, Meshkov AN, Savkin IA, Katkova EV, Kutov DC, Hasanova ZB, Konovalova NV, Kukharchuk VV, Sulimov VB. Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with bayesian networks. J Comp Eng Math. 2015;2(4):11-26. doi: 10.14529/jcem150402
14. Vanagas G. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact Cardiovasc Thorac Surg. 2004;3(2):319-22. doi: 10.1016/j.icvts.2004.01.008
15. Tung BY, Emond MJ, Bronner MP, Raaka SD, Cotler SJ, Kowdley KV. Hepatitis C, iron status, and disease severity: relationship with HFE mutations. Gastroenterology. 2003;124(2):318-26. doi: 10.1053/gast.2003.50046
16. Barbaro G, Di Lorenzo G, Ribersani M, Soldini M, Giancaspro G, Bellomo G, Belloni G, Grisorio B, Barbarini G. Serum ferritin and hepatic glutathione concentrations in chronic hepatitis C patients related to the hepatitis C virus genotype. J Hepatol. 1999;30(5):774-82.
17. Erhardt A, Maschner-Olberg A, Mellenthin C, Kappert G, Adams O, Donner A, Willers R, Niederau C, Haussinger D. HFE mutations and chronic hepatitis C: H63D and C282Y heterozygosity are independent risk factors for liver fibrosis and cirrhosis. J Hepatol. 2003;38(3):335-42.
18. Moreno MU, José GS, Fortuño A, Beloqui O, Díez J, Zalba G. The C242T CYBA polymorphism of NADPH oxidase is associated with essential hypertension. J Hypertens. 2006;24(7):1299-306. doi: 10.1097/01.hjh.0000234110.54110.56
19. Altarescu G, Haim S, Elstein D. Angiotensinogen promoter and angiotensinogen II receptor type 1 gene polymorphisms and incidence of ischemic stroke and neurologic phenotype in Fabry disease. Biomarkers. 2013;18(7):595-600. doi: 10.3109/1354750X.2013.836244
20. Anstee QM, Dhar A, Thursz MR. The role of hypercoagulability in liver fibrogenesis. Clin Res Hepatol Gastroenterol. 2011;35(8-9):526-33. doi: 10.1016/j.clinre.2011.03.011
21. Plompen EP, Darwish Murad S, Hansen BE, Loth DW, Schouten JN, Taimr P, Hofman A, Uitterlinden AG, Stricker BH, Janssen HL, Leebeek FW. Prothrombotic Genetic Risk Factors are associated with an Increased Risk of Liver Fibrosis in the General Population: The Rotterdam Study. J Hepatol. 2015;63(6):1459-65. doi: 10.1016/j.jhep.2015.07.026
22. Wright M, Goldin R, Hellier S, Knapp S, Frodsham A, Hennig B, Hill A, Apple R, Cheng S, Thomas H, Thursz M. Factor V Leiden polymorphism and the rate of fibrosis development in chronic hepatitis C virus infection. Gut. 2003;52(8):1206-10.
23. Bochud PY, Cai T, Overbeck K, Bochud M, Dufour JF, Mullhaupt B, Borovicka J, Heim M, Moradpour D, Cerny A, Malinverni R, Francioli P, Negro F. Genotype 3 is associated with accelerated fibrosis progression in chronic hepatitis C. J Hepatol. 2009;51(4):655-66. doi: 10.1016/j.jhep.2009.05.016
24. Dammacco F, Sansonno D. Therapy for hepatitis C virus-related cryoglobulinemic vasculitis. N Engl J Med. 2013;369(11):1035-45. doi: 10.1056/NEJMra1208642
25. Saadoun D, Asselah T, Resche-Rigon M, Charlotte F, Bedossa P, Valla D, Piette JC, Marcellin P, Cacoub P. Cryoglobulinemia is associated with steatosis and fibrosis in chronic hepatitis C. Hepatology. 2006;43:1337-45. doi: 10.1002/hep.21190
26. Casato M, Carlesimo M, Francia A, Timarco C, Antenucci A, Bove M, Martini H, Visentini M, Fiorilli M, Conti L. Influence of inherited and acquired thrombophilic defects on the clinical manifestations of mixed cryoglobulinaemia. Rheumatology (Oxford). 2008;47:1659-63. doi: 10.1093/rheumatology/ken303
2. Сулимов А.В., Втюрина Д.Н., Романов А.Н., Масленников Е.Д., Сулимов В.Б., Курочкин И.Н., Упоров И.В., Затейщиков Д.А., Носиков В.В., Варфоломеев С.Д. Экспертные системы персонифицированной медицины: применение байесовских сетей для предсказания состояния пациентов. В кн.: Пост-геномные исследования и технологии (под ред. чл.-корр. РАН С.Д. Варфоломеева). Москва: Изд-во МГУ; 2011. С. 641-702 [Sulimov AV, Vtyurina DN, Romanov AN, Maslennikov ED, Sulimov VB, Kurochkin IN, Uporov IV, Zateystchikov DA, Nosikov VV, Varfolomeev SD. Expert systems of personalized medicine: using Bayesian networks for prediction of patients condition. In: Post-genomnye issledovaniya i tekhnologii [Post-genomic investigations and technologies] (edited by a corresponding member of the Russian Academy of Sciences SD Varfolomeev). Moscow: MSU publishing house; 2011. P. 641-702 (In Russ.)].
3. Генс Г.П., Сулимов А.В., Моисеева Н.И., Каткова Е.В., Вельшер Л.З., Коробкова Л.И., Савкин И.А., Сулимов В.Б. Применение генных сигнатур и медицинских экспертных систем для прогнозирования клинических исходов рака молочной железы. Вестник РОНЦ им. Н.Н. Блохина РАМН. 2015;26(4):16-33 [Gens GP, Sulimov AV, Moiseeva NI, Katkova EV, Velsher LZ, Korobkova LI, Savkin IA, Sulimov VB. Application of gene signatures and medical expert systems for prediction of the clinical outcomes of breast cancer. Vestnic RONC im. N.N. Blochina RAMN. 2015;26(4):16-33 (In Russ.)].
4. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian network sinbiomedicine andhealth-care. Artif Intell Med. 2004;30(3):201-14. doi: 10.1016/j.artmed.2003.11.001
5. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006;22(14):184-90. doi: 10.1093/bioinformatics/btl230
6. Poynard T, Ratziu V, Charlotte F, Goodman Z, Mchutchison J, Albrecht J. Rates and risk factors of liver fibrosis progression in patients with chronic hepatitis C. J Hepatol. 2001;34(5):730-9.
7. Jensen FV, Nielsen TD. Bayesian Networks and Decision Graphs. New York: Springer Verlag; 2007: 193 p.
8. Obuchowski NA. ROC analysis. Am J Roentgenol. 2005;184(2):364-72. doi: 10.2214/ajr.184.2.01840364
9. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi: 10.1148/radiology.143.1.7063747
10. Maslennikov ED, Sulimov AV, Savkin IA, Evdokimova MA, Zateyshchikov DA, Nosikov VV, Sulimov VB. An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine. J Applied Statistics. 2015;42(1):71-87. doi: 10.1080/02664763.2014.934664
11. Генс Г.П., Сулимов А.В., Моисеева Н.И., Овсий О.Г., Вельшер Л.З., Рыбалкина Е.Ю., Селезнева И.И., Савкин И.А., Сулимов В.Б. Поиск подходов к прогнозированию исходов рака молочной железы с помощью байесовских сетей. Онкология. 2014;3(5):37-46 [Gens GP, Sulimov AV, Moiseeva NI, Ovsiy OG, Velsher LZ, Rybalkina EYu, Seleznyova II, Savkin IA, Sulimov VB. Search for approaches for prediction of breast cancer using Bayesian networks. Onkologiya. 2014;3(5):37-46 (In Russ.)].
12. Топтыгина А.П., Азиатцева В.В., Савкин И.А., Кислицин А.А., Семикина Е.Л., Гребенников Д.С., Алешкин А.В., Сулимов А.В., Сулимов В.Б., Бочаров Г.А. Прогнозирование специфического гуморального иммунного ответа на основании исходных параметров иммунного статуса детей, привитых против кори, краснухи и эпидемического паротита. Иммунология. 2015;36(1):22-30 [Toptygina AP, Aziatceva VV, Savkin IA, Kislitsyn AA, Semikina EL, Grebennikov DS, Aleshkin AV, Sulimov AV, Sulimov VB. Prediction of a specific humoral immune response based on the initial parameters of the immune status of children vaccinated against measles, rubella and mumps. Immunologiya. 2015;36(1):22-30 (In Russ.)].
13. Sulimov AV, Meshkov AN, Savkin IA, Katkova EV, Kutov DC, Hasanova ZB, Konovalova NV, Kukharchuk VV, Sulimov VB. Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with bayesian networks. J Comp Eng Math. 2015;2(4):11-26. doi: 10.14529/jcem150402
14. Vanagas G. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact Cardiovasc Thorac Surg. 2004;3(2):319-22. doi: 10.1016/j.icvts.2004.01.008
15. Tung BY, Emond MJ, Bronner MP, Raaka SD, Cotler SJ, Kowdley KV. Hepatitis C, iron status, and disease severity: relationship with HFE mutations. Gastroenterology. 2003;124(2):318-26. doi: 10.1053/gast.2003.50046
16. Barbaro G, Di Lorenzo G, Ribersani M, Soldini M, Giancaspro G, Bellomo G, Belloni G, Grisorio B, Barbarini G. Serum ferritin and hepatic glutathione concentrations in chronic hepatitis C patients related to the hepatitis C virus genotype. J Hepatol. 1999;30(5):774-82.
17. Erhardt A, Maschner-Olberg A, Mellenthin C, Kappert G, Adams O, Donner A, Willers R, Niederau C, Haussinger D. HFE mutations and chronic hepatitis C: H63D and C282Y heterozygosity are independent risk factors for liver fibrosis and cirrhosis. J Hepatol. 2003;38(3):335-42.
18. Moreno MU, José GS, Fortuño A, Beloqui O, Díez J, Zalba G. The C242T CYBA polymorphism of NADPH oxidase is associated with essential hypertension. J Hypertens. 2006;24(7):1299-306. doi: 10.1097/01.hjh.0000234110.54110.56
19. Altarescu G, Haim S, Elstein D. Angiotensinogen promoter and angiotensinogen II receptor type 1 gene polymorphisms and incidence of ischemic stroke and neurologic phenotype in Fabry disease. Biomarkers. 2013;18(7):595-600. doi: 10.3109/1354750X.2013.836244
20. Anstee QM, Dhar A, Thursz MR. The role of hypercoagulability in liver fibrogenesis. Clin Res Hepatol Gastroenterol. 2011;35(8-9):526-33. doi: 10.1016/j.clinre.2011.03.011
21. Plompen EP, Darwish Murad S, Hansen BE, Loth DW, Schouten JN, Taimr P, Hofman A, Uitterlinden AG, Stricker BH, Janssen HL, Leebeek FW. Prothrombotic Genetic Risk Factors are associated with an Increased Risk of Liver Fibrosis in the General Population: The Rotterdam Study. J Hepatol. 2015;63(6):1459-65. doi: 10.1016/j.jhep.2015.07.026
22. Wright M, Goldin R, Hellier S, Knapp S, Frodsham A, Hennig B, Hill A, Apple R, Cheng S, Thomas H, Thursz M. Factor V Leiden polymorphism and the rate of fibrosis development in chronic hepatitis C virus infection. Gut. 2003;52(8):1206-10.
23. Bochud PY, Cai T, Overbeck K, Bochud M, Dufour JF, Mullhaupt B, Borovicka J, Heim M, Moradpour D, Cerny A, Malinverni R, Francioli P, Negro F. Genotype 3 is associated with accelerated fibrosis progression in chronic hepatitis C. J Hepatol. 2009;51(4):655-66. doi: 10.1016/j.jhep.2009.05.016
24. Dammacco F, Sansonno D. Therapy for hepatitis C virus-related cryoglobulinemic vasculitis. N Engl J Med. 2013;369(11):1035-45. doi: 10.1056/NEJMra1208642
25. Saadoun D, Asselah T, Resche-Rigon M, Charlotte F, Bedossa P, Valla D, Piette JC, Marcellin P, Cacoub P. Cryoglobulinemia is associated with steatosis and fibrosis in chronic hepatitis C. Hepatology. 2006;43:1337-45. doi: 10.1002/hep.21190
26. Casato M, Carlesimo M, Francia A, Timarco C, Antenucci A, Bove M, Martini H, Visentini M, Fiorilli M, Conti L. Influence of inherited and acquired thrombophilic defects on the clinical manifestations of mixed cryoglobulinaemia. Rheumatology (Oxford). 2008;47:1659-63. doi: 10.1093/rheumatology/ken303
________________________________________________
2. Sulimov AV, Vtyurina DN, Romanov AN, Maslennikov ED, Sulimov VB, Kurochkin IN, Uporov IV, Zateystchikov DA, Nosikov VV, Varfolomeev SD. Expert systems of personalized medicine: using Bayesian networks for prediction of patients condition. In: Post-genomnye issledovaniya i tekhnologii [Post-genomic investigations and technologies] (edited by a corresponding member of the Russian Academy of Sciences SD Varfolomeev). Moscow: MSU publishing house; 2011. P. 641-702 (In Russ.)
3. Gens GP, Sulimov AV, Moiseeva NI, Katkova EV, Velsher LZ, Korobkova LI, Savkin IA, Sulimov VB. Application of gene signatures and medical expert systems for prediction of the clinical outcomes of breast cancer. Vestnic RONC im. N.N. Blochina RAMN. 2015;26(4):16-33 (In Russ.)
4. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian network sinbiomedicine andhealth-care. Artif Intell Med. 2004;30(3):201-14. doi: 10.1016/j.artmed.2003.11.001
5. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006;22(14):184-90. doi: 10.1093/bioinformatics/btl230
6. Poynard T, Ratziu V, Charlotte F, Goodman Z, Mchutchison J, Albrecht J. Rates and risk factors of liver fibrosis progression in patients with chronic hepatitis C. J Hepatol. 2001;34(5):730-9.
7. Jensen FV, Nielsen TD. Bayesian Networks and Decision Graphs. New York: Springer Verlag; 2007: 193 p.
8. Obuchowski NA. ROC analysis. Am J Roentgenol. 2005;184(2):364-72. doi: 10.2214/ajr.184.2.01840364
9. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi: 10.1148/radiology.143.1.7063747
10. Maslennikov ED, Sulimov AV, Savkin IA, Evdokimova MA, Zateyshchikov DA, Nosikov VV, Sulimov VB. An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine. J Applied Statistics. 2015;42(1):71-87. doi: 10.1080/02664763.2014.934664
11. Gens GP, Sulimov AV, Moiseeva NI, Ovsiy OG, Velsher LZ, Rybalkina EYu, Seleznyova II, Savkin IA, Sulimov VB. Search for approaches for prediction of breast cancer using Bayesian networks. Onkologiya. 2014;3(5):37-46 (In Russ.)
12. Toptygina AP, Aziatceva VV, Savkin IA, Kislitsyn AA, Semikina EL, Grebennikov DS, Aleshkin AV, Sulimov AV, Sulimov VB. Prediction of a specific humoral immune response based on the initial parameters of the immune status of children vaccinated against measles, rubella and mumps. Immunologiya. 2015;36(1):22-30 (In Russ.)
13. Sulimov AV, Meshkov AN, Savkin IA, Katkova EV, Kutov DC, Hasanova ZB, Konovalova NV, Kukharchuk VV, Sulimov VB. Genome-wide analysis of genetic associations for prediction of polygenic hypercholesterolemia with bayesian networks. J Comp Eng Math. 2015;2(4):11-26. doi: 10.14529/jcem150402
14. Vanagas G. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact Cardiovasc Thorac Surg. 2004;3(2):319-22. doi: 10.1016/j.icvts.2004.01.008
15. Tung BY, Emond MJ, Bronner MP, Raaka SD, Cotler SJ, Kowdley KV. Hepatitis C, iron status, and disease severity: relationship with HFE mutations. Gastroenterology. 2003;124(2):318-26. doi: 10.1053/gast.2003.50046
16. Barbaro G, Di Lorenzo G, Ribersani M, Soldini M, Giancaspro G, Bellomo G, Belloni G, Grisorio B, Barbarini G. Serum ferritin and hepatic glutathione concentrations in chronic hepatitis C patients related to the hepatitis C virus genotype. J Hepatol. 1999;30(5):774-82.
17. Erhardt A, Maschner-Olberg A, Mellenthin C, Kappert G, Adams O, Donner A, Willers R, Niederau C, Haussinger D. HFE mutations and chronic hepatitis C: H63D and C282Y heterozygosity are independent risk factors for liver fibrosis and cirrhosis. J Hepatol. 2003;38(3):335-42.
18. Moreno MU, José GS, Fortuño A, Beloqui O, Díez J, Zalba G. The C242T CYBA polymorphism of NADPH oxidase is associated with essential hypertension. J Hypertens. 2006;24(7):1299-306. doi: 10.1097/01.hjh.0000234110.54110.56
19. Altarescu G, Haim S, Elstein D. Angiotensinogen promoter and angiotensinogen II receptor type 1 gene polymorphisms and incidence of ischemic stroke and neurologic phenotype in Fabry disease. Biomarkers. 2013;18(7):595-600. doi: 10.3109/1354750X.2013.836244
20. Anstee QM, Dhar A, Thursz MR. The role of hypercoagulability in liver fibrogenesis. Clin Res Hepatol Gastroenterol. 2011;35(8-9):526-33. doi: 10.1016/j.clinre.2011.03.011
21. Plompen EP, Darwish Murad S, Hansen BE, Loth DW, Schouten JN, Taimr P, Hofman A, Uitterlinden AG, Stricker BH, Janssen HL, Leebeek FW. Prothrombotic Genetic Risk Factors are associated with an Increased Risk of Liver Fibrosis in the General Population: The Rotterdam Study. J Hepatol. 2015;63(6):1459-65. doi: 10.1016/j.jhep.2015.07.026
22. Wright M, Goldin R, Hellier S, Knapp S, Frodsham A, Hennig B, Hill A, Apple R, Cheng S, Thomas H, Thursz M. Factor V Leiden polymorphism and the rate of fibrosis development in chronic hepatitis C virus infection. Gut. 2003;52(8):1206-10.
23. Bochud PY, Cai T, Overbeck K, Bochud M, Dufour JF, Mullhaupt B, Borovicka J, Heim M, Moradpour D, Cerny A, Malinverni R, Francioli P, Negro F. Genotype 3 is associated with accelerated fibrosis progression in chronic hepatitis C. J Hepatol. 2009;51(4):655-66. doi: 10.1016/j.jhep.2009.05.016
24. Dammacco F, Sansonno D. Therapy for hepatitis C virus-related cryoglobulinemic vasculitis. N Engl J Med. 2013;369(11):1035-45. doi: 10.1056/NEJMra1208642
25. Saadoun D, Asselah T, Resche-Rigon M, Charlotte F, Bedossa P, Valla D, Piette JC, Marcellin P, Cacoub P. Cryoglobulinemia is associated with steatosis and fibrosis in chronic hepatitis C. Hepatology. 2006;43:1337-45. doi: 10.1002/hep.21190
26. Casato M, Carlesimo M, Francia A, Timarco C, Antenucci A, Bove M, Martini H, Visentini M, Fiorilli M, Conti L. Influence of inherited and acquired thrombophilic defects on the clinical manifestations of mixed cryoglobulinaemia. Rheumatology (Oxford). 2008;47:1659-63. doi: 10.1093/rheumatology/ken303
Авторы
Л.М. Самоходская1, Е.Е. Старостина1, А.В. Сулимов1, Т.Н. Краснова1, Т.П. Розина1,2, В.Г. Авдеев1, И.А. Савкин1, В.Б. Сулимов1, Н.А. Мухин1,2, В.А. Ткачук1, В.А. Садовничий1
1 ФГБОУ ВО «Московский государственный университет им. М.В. Ломоносова», Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
1 M.V. Lomonosov Moscow State University, Moscow, Russia;
2 I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
1 ФГБОУ ВО «Московский государственный университет им. М.В. Ломоносова», Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
________________________________________________
1 M.V. Lomonosov Moscow State University, Moscow, Russia;
2 I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
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