informИнформатикаInformatics1816-03012617-6963UIIP NASB10.37661/1816-0301-2021-18-1-105-122inform-1110Research ArticleБИОИНФОРМАТИКАBIOINFORMATICSКомплексный анализ данных при исследовании сложных биомолекулярных системData analysis in complex biomolecular systemsЯцковН. Н.YatskouM. M.

Яцков Николай Николаевич, кандидат физико-математических наук, доцент кафедры системного анализа и компьютерного моделирования, факультет радиофизики и компьютерных технологий

пр. Независимости, 4, 220030

Mikalai M. Yatskou, Cand. Sci. (Phys.-Math.), Associate Professor, Department of Systems Analysis and Computer Modelling, Faculty of Radiophysics and Computer Technologies

av. Nezaliezhnasti, 4, 220030

yatskou@bsu.byАпанасовичВ. В.ApanasovichV. V.

Апанасович Владимир Владимирович, доктор физико-математических наук, профессор

пр. Независимости, 4, 220030

Vladimir V. Apanasovich, Dr. Sci. (Phys.-Math.), Professor

av. Nezaliezhnasti, 4, 220030

apanasovichv@gmail.comБелорусский государственный университетBelarusian State University202129032021181105122Copyright © Яцков Н.Н., Апанасович В.В., 20212021Яцков Н.Н., Апанасович В.В.Yatskou M.M., Apanasovich V.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/1110

Развитие биомолекулярных технологий напрямую связано с разработкой эффективных методов и алгоритмов обработки большого объема информации, получаемой с помощью современного высокопроизводительного экспериментального оборудования. В числе приоритетных задач – разработка перспективных инструментов анализа и интерпретации биофизической информации с использованием методов анализа больших данных и компьютерных моделей.

Предложен комплексный подход к обработке больших наборов данных на основе методов интеллектуального анализа данных и имитационного моделирования, позволяющий определять параметры биофизических и оптических процессов, происходящих в сложных биомолекулярных системах. Идея комплексного подхода состоит в использовании имитационного моделирования биофизических процессов, протекающих в объекте исследования, сравнении отобранных методами снижения размерности смоделированных и наиболее информативных экспериментальных данных, определении характеристик исследуемых процессов с применением алгоритмов интеллектуального анализа данных.

Рассмотрено применение разработанного подхода для исследования бимолекулярных систем в экспериментах флуоресцентной спектроскопии. Эффективность алгоритмов подхода проверена в ходе анализа смоделированных и экспериментальных данных, представляющих системы молекул и белков. Применение комплексного анализа повышает эффективность исследования биофизических систем в ходе анализа больших данных.

The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysical and optical processes occurring in complex biomolecular systems. The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. The application of the developed approach to the study of bimolecular systems in fluorescence spectroscopy experiments is considered. The effectiveness of the algorithms of the approach was verified by analyzing of simulated and experimental data representing the systems of molecules and proteins. The use of complex analysis increases the efficiency of the study of biophysical systems during the analysis of big data.

биомолекулярная системабиофизические процессыимитационное моделированиеинтеллектуальный анализ данныхфлуоресцентная спектроскопия с временным разрешениемфлуорес- центная флуктуационная спектроскопияbiomolecular systembiophysical processessimulation modellingdata analysistime-resolved fluorescence spectroscopyfluorescence fluctuation spectroscopyReferencesКомпьютерные технологии и анализ данных (CTDA’2020) : материалы II Междунар. науч.-практ. конф., Минск, 23–24 апр. 2020 г. / Белорус. гос. ун-т ; редкол. : В. В. Скакун (отв. ред.) [и др.]. – Минск : БГУ, 2020. – 299 с.Skakun V. V., Yatskou M. M., Lutkovski V. M., Lisitsa Y. V., Eismont I. S. (eds.). Kompyuternye tehnologii i analiz dannyh (CTDA’2020) : materialy II Mezhdunarodnoj nauchno-prakticheskoj konferencii, Minsk, 23–24 aprelya 2020 [Computer Technologies and Data Analysis (CTDA’2020)] : Proceedings of the II International Scientific Conference, Minsk, 23–24 April 2020]. Minsk, Belorusckij gosudarstvennyj universitet, 2020, 299 p. (in Russian).Квантовая электроника : материалы XII Междунар. науч.-техн. конф., Минск, 18–22 нояб. 2019 г. / редкол. : М. М. Кугейко (отв. ред.) [и др.]. – Минск : РИВШ, 2019. – 320 с.Kugeiko M. M., Afonenko A. A., Barkova A. V. Kvantovaya elektronika : materialy XII Mezhdunarodnoj nauchno-tehnicheskoj konferencii, Minsk, 18–22 noyabrya 2019 [Quantum Electronics: Proceedings of the XII International Scientific and Practical Conference, Minsk, 18–22 November 2019]. Minsk, Respublikanskij institut vysshej shkoly, 2019, 320 p. (in Russian).Информационные технологии и системы (ИТС 2019) : материалы Междунар. науч. конф., БГУИР, Минск, 30 окт. 2019 г. / редкол. : Л. Ю. Шилин [и др.]. – Минск : БГУИР, 2019. – 305 с.Shilin L. U., Kukin D. P., Ivaniuk А. А., Markov A. V., Golenkov V. V., ..., Gurinovich A. B. (eds.). Informacionnye tehnologii i sistemy (ITS 2019): materialy Mezhdunarodnoj nauchnoj konferencii, Minsk, 30 oktjabrja 2019 [Information Technologies and Systems 2019 (ITS 2019): Proceedings of the International Scientific Conference, Minsk, 30 October 2019]. Minsk, Belorusskij gosudarstvennyj universitet informatiki i radioelektroniki, 2019, 305 p. (in Russian).Chen, J. Integrations between autonomous systems and modern computing techniques: a mini review / J. Chen, M. Abbod, J.-S. Shieh // Sensors. – September 2019. – Vol. 19, iss. 18. – Р. 3897.Chen J., Abbod M., Shieh J.-S. Integrations between autonomous systems and modern computing techniques: a mini review. Sensors, September 2019, vol. 19, iss. 18, рр. 3897. https://doi.org/10.3390/s19183897Klinger, C. M. Small genomes and big data: adaptation of plastid genomics to the high-throughput era / C. M. Klinger, E. Richardson // Biomolecules. – July 2019. – Vol. 9, iss. 8. – Р. 299.Klinger C. M., Richardson E. Small genomes and big data: adaptation of plastid genomics to the high-throughput era. Biomolecules, July 2019, vol. 9, iss. 8, рр. 299. https://doi.org/10.3390/biom9080299The potential use of big data in oncology / S. M. Willems [et al.] // Oral Oncology. – 2019. – Vol. 98. – P. 8–12.Willems S. M., Abeln S., Feenstra K. A., de Bree R., van der Poel E. F., ..., van den Brekel M. W. M. The potential use of big data in oncology. Oral Oncology, 2019, vol. 98, pp. 8–12.Bramer, M. Principles of Data Mining / M. Bramer. – 2nd ed. – London : Springer, 2013. – 440 p.Bramer M. Principles of Data Mining. 2nd ed. London, Springer, 2013, 440 p.Aggarwal, C. C. Data Mining: The Textbook / C. C. Aggarwal. – Gewerbestrasse : Springer, 2015. – 734 p.Aggarwal C. C. Data Mining: The Textbook. Gewerbestrasse, Springer, 2015, 734 p.Hastie, T. The Elements of Statistical Learning. Data Mining, Inference, and Prediction / T. Hastie, R. Tibshirani, J. Friedman. – 2nd ed. – N. Y. : Springer, 2009. – 739 p.Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd ed. New York, Springer, 2009, 739 p.Machine learning approaches for the estimation of biological aging: the road ahead for population studies / A. Gialluisi [et al.] // Frontiers in Medicine. – July 2019. – Vol. 6. – P. 146.Gialluisi A., Di Castelnuovo A., Donati M. B., de Gaetano G., Iacoviello L., the Moli-sani Study Investigators. Machine learning approaches for the estimation of biological aging: the road ahead for population studies. Frontiers in Medicine, July 2019, vol. 6, pp. 146. https://doi.org/10.3389/fmed.2019.00146Jiang, P. Big data approaches for modeling response and resistance to cancer drugs / P. Jiang, W. R. Sellers, X. S. Liu // Annual Review of Biomedical Data Science. – July 2018. – Vol. 1. – Р. 1–27.Jiang P., Sellers W. R., Liu X. S. Big data approaches for modeling response and resistance to cancer drugs. Annual Review of Biomedical Data Science, July 2018, vol. 1, рр. 1–27. https://doi.org/10.1146/annurev-biodatasci-080917-013350Duran, J. M. Computer Simulations in Science and Engineering / J. M. Duran. – Springer International Publishing, 2018. – 209 p.Duran J. M. Computer Simulations in Science and Engineering. Springer International Publishing, 2018, 209 p.Zeigler, B. P. Theory of Modeling and Simulation / B. P. Zeigler, A. Muzy, E. Kofman. – 3rd ed. – Academic Press, 2018. – 692 p.Zeigler B. P., Muzy A., Kofman E. Theory of Modeling and Simulation. 3rd ed. Academic Press, 2018, 692 p.Toward computational modelling on immune system function / F. Pappalardo [et al.] // BMC Bioinformatics. – December 2019. – Vol. 20, suppl. 6. – Р. 622.Pappalardo F., Pennisi M., Reche P. A., Russo G. Toward computational modelling on immune system function. BMC Bioinformatics, December 2019, vol. 20, suppl. 6, рр. 622. https://doi.org/10.1186/s12859-020-03897-5Computational models of epileptiform activity / F. Wendling [et al.] // J. of Neuroscience Methods. – 2016. – Vol. 260. – P. 233–251.Wendling F., Benquet P., Bartolomei F., Jirsa V. Computational models of epileptiform activity. Journal of Neuroscience Methods, 2016, vol. 260, pp. 233–251.Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification / J. E. Sager [et al.] // Drug Metabolism and Disposition. – 2015. – Vol. 43. – P. 1823–1837.Sager J. E., Yu J., Ragueneau-Majlessi I., Isoherranen N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metabolism and Disposition, 2015, vol. 43, pp. 1823–1837.Modelling the effect of compliance with nordic nutrition recommendations on cardiovascular disease and cancer mortality in the nordic countries / S. Saha [et al.] // Nutrients. – June 2019. – Vol. 11, no. 6. – Р. 1434.Saha S., Nordström J., Mattisson I., Nilsson P. M., Gerdtham U.-G. Modelling the effect of compliance with nordic nutrition recommendations on cardiovascular disease and cancer mortality in the nordic countries. Nutrients, June 2019, vol. 11, no. 6, рр. 1434. https://doi.org/10.3390/nu11061434Periyasamy, V. Advances in Monte Carlo simulation for light propagation in tissue / V. Periyasamy, M. Pramanik // IEEE Reviews in Biomedical Engineering. – 2017. – Vol. 10. – P. 122–135.Periyasamy V., Pramanik M. Advances in Monte Carlo simulation for light propagation in tissue. IEEE Reviews in Biomedical Engineering, 2017, vol. 10, pp. 122–135.Subramanian, A. S. R. Modeling and simulation of energy systems: a review / A. S. R. Subramanian, T. Gundersen, T. A. Adams // Processes. – November 2018. – Vol. 6, iss. 12. – Р. 238.Subramanian A. S. R., Gundersen T., Adams T. A. Modeling and simulation of energy systems: a review. Processes, November 2018, vol. 6, iss. 12, рр. 238. https://doi.org/10.3390/pr6120238Modeling and simulation as support for development of human health space exploration projects / A. G. Bruzzone [et al.] // Proc. of the 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, the 57th SIMS Conf. on Simulation and Modelling SIMS 2016, Oulu, Finland, 12–16 Sept. 2016. – Oulu, 2016. – P. 1109–1115.Bruzzone A. G., Massei M., Mùrino G., Di Matteo R., Agresta M., Maglione G. L. Modeling and simulation as support for development of human health space exploration projects. Proceedings of the 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, the 57th SIMS Conference on Simulation and Modelling SIMS 2016, Oulu, Finland, 12–16 September 2016. Oulu, 2016, pp. 1109–1115.Fluorescence Spectroscopy and Microscopy: Methods and Protocols. Methods in Molecular Biology / eds. Y. Engelborghs, A. J. W. G. Visser. – Heidelberg : Springer Science+Business Media, LLC, 2014. – 816 p.Engelborghs Y., Visser A. J. W. G. (eds.). Fluorescence Spectroscopy and Microscopy: Methods and Protocols. Methods in Molecular Biology. Heidelberg, Springer Science + Business Media, LLC, 2014, 816 p.Yatskou, M. Сomputer Simulation of Energy Relaxation and Transport in Organized Porphyrin Systems / M. Yatskou. – The Netherlands, Wageningen : Ponsen & Looijen Printing Establishment, 2001. – 176 p.Yatskou M. Сomputer Simulation of Energy Relaxation and Transport in Organized Porphyrin Systems. The Netherlands, Wageningen, Ponsen & Looijen Printing Establishment, 2001, 176 p.Комплексный анализ данных при исследовании сложных биомолекулярных систем / Н. Н. Яцков, В. В. Апанасович // Квантовая электроника : материалы XII Междунар. науч.-техн. конф., Минск, 18–22 нояб. 2019 г. – Минск : РИВШ, 2019. – С. 282–283.Yatskou M. M., Apanasovich V. V. Kompleksnyj analiz dannyh pri issledovanii slozhnyh biomolekulyarnyh sistem [Data analysis of complex biomolecular systems]. Kvantovaya elektronika : materialy XII Mezhdunarodnoj nauchno-tehnicheskoj konferencii, Minsk, 18–22 noyabrya 2019 [Quantum Electronics: Proceedings of the XII International Scientific and Practical Conference, Minsk, 18–22 November 2019]. Minsk, Respublikanskij institut vysshej shkoly, 2019, pp. 282–283 (in Russian).Исследование биофизических систем с использованием алгоритмов интеллектуального анализа данных и имитационного моделирования / Н. Н. Яцков, В. В. Апанасович // Компьютерные технологии и анализ данных (CTDA’2020) : материалы II Междунар. науч.-практ. конф., Минск, 23–24 апр. 2020 г. – Минск : БГУ, 2020. – С. 120–123.Yatskou M. M., Apanasovich V. V. Issledovanie biofizicheskih sistem s ispolzovaniem algoritmov intellektualnogo analiza dannyh i imitacionnogo modelirovaniya [Investigation of biophysical systems using data mining and simulation modeling algorithms]. Kompyuternye tehnologii i analiz dannyh (CTDA’2020) : materialy II Mezhdunarodnoj nauchno-prakticheskoj konferencii, Minsk, 23–24 aprelya 2020 [Computer Technologies and Data Analysis (CTDA’2020) : Proceedings of the II International Scientific Conference, Minsk, 23–24 April 2020]. Minsk, Belorusckij gosudarstvennyj universitet, 2020, pp. 120–123 (in Russian).Tang, J. Simulation and Computational Red Teaming for Problem Solving / J. Tang, G. Leu, H. A. Abbass. – New Jersey : John Wiley & Sons, Inc., 2020. – 464 p.Tang J., Leu G., Abbass H. A. Simulation and Computational Red Teaming for Problem Solving. New Jersey, John Wiley & Sons, Inc., 2020, 464 p.Design and evaluation of actichip, a thematic microarray for the study of the actin cytoskeleton / J. Muller [et al.] // BMC Genomics. – Aug. 2007. – Vol. 8. – Р. 294.Muller J., Mehlen A., Vetter G., Yatskou M., Muller A., ..., Vallar L. Design and evaluation of actichip, a thematic microarray for the study of the actin cytoskeleton. BMC Genomics, August 2007, vol. 8, рр. 294. https://doi.org/10.1186/1471-2164-8-294Advanced spot quality analysis in two-colour microarray experiments / M. Yatskou [et al.] // BMC Research Notes. – Sept. 2008. – Vol. 1. – Р. 80.Yatskou M., Novikov E., Vetter G., Muller A., Barillot E., ..., Friederich E. Advanced spot quality analysis in two-colour microarray experiments. BMC Research Notes, September 2008, vol. 1, рр. 80. https://doi.org/10.1186/1756-0500-1-80Al-Ajlan, A. Feature selection for gene prediction in metagenomic fragments / A. Al-Ajlan, A. El Allali // BioData Mining. – 2018. – Vol. 11. – Р. 9.Al-Ajlan A., El Allali A. Feature selection for gene prediction in metagenomic fragments. BioData Mining, 2018, vol. 11, рр. 9. https://doi.org/10.1186/s13040-018-0170-zРазработка алгоритмов и программных средств классификации кодирующих и некодирующих нуклеотидных последовательностей / В. Р. Закирова [и др.] // Информатика. – 2019. – Т. 16, № 2. – С. 111–120.Zakirava V. R., Syrokvash D. A., Hileuski S. V., Nazarov P. V., Yatskou M. M. Razrabotka algoritmov i programmnyh sredstv klassifikacii kodiruyushih i nekodiruyushih nukleotidnyh posledovatelnostej [Development of algorithms and software for classification of nucleotide sequences]. Informatika [Informatics], 2019, vol. 16, no. 2, pp. 111–120 (in Russian).Яцков, Н. Н. Интеллектуальный анализ данных : пособие / Н. Н. Яцков. – Минск : БГУ, 2014. – 151 с.Yatskou M. M. Intellektualnyj analiz dannyh. Data Mining. Minsk, Belorusckij gosudarstvennyj universitet, 2014, 151 p. (in Russian).Шакла, Н. Машинное обучение и TensorFlow / Н. Шакла. – СПб. : Питер, 2019. – 336 с.Shukla N. Mashinnoe obuchenie i TensorFlow. Machine Learning with TensorFlow. Saint Petersburg, Piter, 2019, 336 p. (in Russian).Исследование методов классификации для анализа сегментированных объектов на люминесцентных изображениях раковых клеток / Е. В. Лисица [и др.] // Вестник Полоцкого гос. ун-та. Сер. C. Фундаментальные науки. – 2020. – № 4. – С. 15–22.Lisitsa Y. V., Yatskou M. M., Skakun V. V., Apanasovich V. V. Issledovanie metodov klassifikacii dlya analiza segmentirovannyh obektov na lyuminescentnyh izobrazheniyah rakovyh kletok [Classification methods for the analysis of segmented objects on fluorescent images of cancer cells]. Vestnik Polockogo gosudarstvennogo universiteta. Seriya C. Fundamentalnye nauki [Vestnik of Polotsk State University. Part C. Fundamental Sciences], 2020, no. 4, pp. 15–22 (in Russian).Изучение структуры Zn-порфиринов методами флуоресцентной спектроскопии / В. В. Апанасович [и др.] // Журнал прикладной спектроскопии. – 1999. – Т. 66, № 4. – C. 549–552.Apanasovich V. V., Novikov E. G., Yatskov N. N., Koehorst R. B. M., Schaafsma T. J., van Hoek A. Study of the Zn-porphyrin structure by fluorescence spectroscopy methods. Journal of Applied Spectroscopy, 1999, vol. 66, no. 4, pp. 549–552.Excitation energy migration in a photonic dye-zeolite antenna: computational techniques / M. Meyer [et al.] // J. of Computational Methods in Science and Engineering. – 2003. – Vol. 3. – P. 395–402.Meyer M., Yatskou M., Pfenninger M., Huber S., Calzaferri G., ..., Apanasovich V. Excitation energy migration in a photonic dye-zeolite antenna: computational techniques. Journal of Computational Methods in Science and Engineering, 2003, vol. 3, pp. 395–402.Dimov, I. T. Monte Carlo Methods for Applied Scientists / I. T. Dimov. – Singapore : World Scientific Publishing Co. Pte. Ltd. – 2008. – 291 p.Dimov I. T. Monte Carlo Methods for Applied Scientists. Singapore, World Scientific Publishing Co. Pte. Ltd., 2008, 291 p.Rubinstein, R. Y. Simulation and the Monte Carlo Method / R. Y. Rubinstein, D. P. Kroese. – 3rd ed. – New Jersey : John Wiley & Sons, Inc., 2017. – 414 p.Rubinstein R. Y., Kroese D. P. Simulation and the Monte Carlo Method. 3rd ed. New Jersey, John Wiley & Sons, Inc., 2017, 414 p.Binder, K. Monte Carlo Simulation in Statistical Physics / K. Binder, D. W. Heermann. – 6th ed. – Springer International Publishing, 2019. – 258 p.Binder K., Heermann D. W. Monte Carlo Simulation in Statistical Physics. 6th ed. Springer International Publishing, 2019, 258 p.A mathematical model of actin filament turnover for fitting FRAP data / A. A. Halavatyi [et al.] // European Biophysical J. – 2010 – Vol. 39, no. 4. – P. 669–677.Halavatyi A. А., Nazarov P. V., Al Tanoury Z., Apanasovich V. V., Yatskou M. M., Friederich E. A mathematical model of actin filament turnover for fitting FRAP data. European Biophysical Journal, 2010, vol. 39, no. 4, pp. 669–677.Apanasovich, V. V. Data analysis in time-resolved fluorescence spectroscopy using computer simulation / V. V. Apanasovich, E. G. Novikov, N. N. Yatskov // Proc. of SPIE. – 1997. – Vol. 2980. – P. 495–502.Apanasovich V. V., Novikov E. G., Yatskov N. N. Data analysis in time-resolved fluorescence spectroscopy using computer simulation. Proceedings of SPIE, 1997, vol. 2980, pp. 495–502.Анализ кинетики затухания флуоресценции сложных молекулярных систем с использованием метода Монте-Карло / В. В. Апанасович, Е. Г. Новиков, Н. Н. Яцков // Журнал прикладной спектроскопии. – 2000. – Т. 67, № 5. – С. 612–618.Apanasovich V. V., Novikov E. G., Yatskov N. N. Analiz kinetiki zatuhanija fluorescencii slozhnyh molekuljarnyh sistem s ispol'zovaniem metoda Monte-Karlo [Analysis of the decay kinetics of fluorescence of complex molecular systems using Monte Carlo method]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2000, vol. 67, no. 5, pp. 612–618 (in Russian).Вьюгин, В. В. Математические основы машинного обучения и прогнозирования / В. В. Вьюгин. – М. : МЦНМО, 2014. – 304 с.Vyugin V. V. Matematicheskie osnovy mashinnogo obucheniya i prognozirovaniya. Mathematical Foundations of Machine Learning and Forecasting. Moscow, Moskovskij centr nepreryvnogo matematicheskogo obrazovanija, 2014, 304 p. (in Russian).Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection / R. Kohavi // Proc. of the 14th Intern. Joint Conf. on Artificial Intelligence IJCAI'95, Montreal, Canada, 20–25 Aug. 1995. – Montreal, 1995. – Vol. 2. – P. 1137–1143.Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence IJCAI'95, Montreal, Canada, 20–25 August 1995. Montreal, 1995, vol. 2, pp. 1137–1143.Encyclopedia of Database Systems / Eds. L. Liu, M. T. Ozsu. – USA : Springer Science+Business Media, LLC, 2009. – 4355 p.Liu L., Ozsu M. T. (eds.). Encyclopedia of Database Systems. USA, Springer Science+Business Media, LLC, 2009, 4355 p.Lakowicz, J. R. Principles of Fluorescence Spectroscopy / J. R. Lakowicz. – 3rd ed. – N. Y. : Springer, 2006. – 954 p.Lakowicz J. R. Principles of Fluorescence Spectroscopy. 3rd ed. New York, Springer, 2006, 954 p.A study of energy transfer processes in zinc-porphyrin films using Monte Carlo simulation of fluorescence decay / M. М. Yatskou [et al.] // Chemical Physics Letters. – 2001. – Vol. 345, no. 1, 2. – P. 141–150.Yatskou M. M., Donker H., Koehorst R. B. M., van Hoek A., Schaafsma T. J. A study of energy transfer processes in zinc-porphyrin films using Monte Carlo simulation of fluorescence decay. Chemical Physics Letters, 2001, vol. 345, no. 1, 2, pp. 141–150.Non-isotropic excitation energy transport in organized molecular systems: Monte Carlo simulation-based analysis of time-resolved fluorescence / M. М. Yatskou [et al.] // J. of Physical Chemistry A. – 2001. – Vol. 105, no. 41. – P. 9498–9508.Yatskou M. M., Donker H., Novikov E. G., Koehorst R. B. M., van Hoek A., ..., Schaafsma T. J. Non-isotropic excitation energy transport in organized molecular systems: Monte Carlo simulation-based analysis of time-resolved fluorescence. Journal of Physical Chemistry A, 2001, vol. 105, no. 41, pp. 9498–9508.Molecular Cell Biology / ed. H. Lodish [et al.]. – 5th ed. – USA : W. H. Freeman Hardcover, 2003. – 973 p.Lodish H., Berk A., Matsudaira P., Kaiser C. A., Krieger M., ..., Darnell J. (eds.). Molecular Cell Biology. 5th ed. USA, W. H. Freeman Hardcover, 2003, 973 p.Molecular Biology of the Gene / J. D. Watson [et al.]. – 6th ed. – USA : Cold Spring Harbor Laboratory Press, 2007. – 880 p.Watson J. D., Baker T. A., Bell S. P., Gann A., Levine M., Losick R. Molecular Biology of the Gene. 6th ed. USA, Cold Spring Harbor Laboratory Press, 2007, 880 p.Льюин, Б. Гены / Б. Льюин ; пер. с 9-го англ. изд. – М. : БИНОМ. Лаборатория знаний, 2012. – 896 с.Lewin B. Genes. 7 ed. Oxford University Press, 1999, 990 р.Shendure, J. Next-generation DNA sequencing / J. Shendure, H. Ji // Nature Biotechnology. – 2008. – Vol. 26, no. 10. – P. 1135–1145.Shendure J., Ji H. Next-generation DNA sequencing. Nature Biotechnology, 2008, vol. 26, no. 10, pp. 1135–1145.Metzker, M. L. Sequencing technologies – the next generation / M. L. Metzker // Nature Reviews Genetics. – 2010. – Vol. 11, no. 1. – P. 31–46.Metzker M. L. Sequencing technologies – the next generation. Nature Reviews Genetics, 2010, vol. 11, no. 1, pp. 31–46.Классификация последовательностей РНК с помощью сверточных нейронных сетей / И. В. Климук [и др.] // Компьютерные технологии и анализ данных (CTDA’2020) : материалы II Междунар. науч.-практ. конф., Минск, 23–24 апр. 2020 г. – Минск : БГУ, 2020. – С. 142–146.Klimuk I. V., Grinev V. V., Skakun V. V., Yatskou M. M. Klassifikaciya posledovatelnostej RNK s pomoshyu svertochnyh nejronnyh setej [RNA sequences classification with convolutional neural networks]. Kompyuternye tehnologii i analiz dannyh (CTDA’2020) : materialy II Mezhdunarodnoj nauchno-prakticheskoj konferencii, Minsk, 23–24 aprelya 2020 [Computer Technologies and Data Analysis (CTDA’2020) : Proceedings of the II International Scientific Conference, Minsk, 23–24 April 2020]. Minsk, Belorusckij gosudarstvennyj universitet, 2020, pp. 142–146 (in Russian).Яцков, Н. Н. Автоматическое определение открытых рамок считывания в молекулах РНК человека с использованием алгоритмов векторизации и классификации / Н. Н. Яцков, В. В. Скакун, В. В. Гринев // Информационные технологии и системы (ИТС 2019) : материалы Междунар. науч. конф., Минск, 30 окт. 2019 г. – Минск : БГУИР, 2019. – С. 292–293.Yatskou M. M., Skakun V. V., Grinev V. V. Avtomaticheskoe opredelenie otkrytyh ramok schityvaniya v molekulah RNK cheloveka s ispolzovaniem algoritmov vektorizacii i klassifikacii [Automatic detection of the open reading frames in human RNA molecules using vectorization and classification algorithms]. Informacionnye tehnologii i sistemy (ITS 2019) : materialy Mezhdunarodnoj nauchnoj konferencii, Minsk, 30 oktjabrja 2019 [Information Technologies and Systems 2019 (ITS 2019): Proceedings of the International Scientific Conference, Minsk, 30 October 2019]. Minsk, Belorusskij gosudarstvennyj universitet informatiki i radioelektroniki, 2019, pp. 292–293 (in Russian).Яцков, Н. Н. Вычислительный подход и программный пакет RNAexploreR для группировки молекул РНК генов человека по их экзонным признакам / Н. Н. Яцков, В. В. Скакун, В. В. Гринев // Информатика. – 2019. – Т. 16, № 4. – С. 7–24.Yatskou M. M., Skakun V. V., Grinev V. V. Vychislitelnyj podhod i programmnyj paket RNAexploreR dlya gruppirovki molekul RNK genov cheloveka po ih ekzonnym priznakam [A computational approach and software package RNAexploreR for grouping RNA molecules of human genes by exon features]. Informatika [Informatics], 2019, vol. 16, no. 4, pp. 7–24 (in Russian).Bumgarner, R. Overview of DNA microarrays: types, applications, and their future / R. Bumgarner // Current Protocols in Molecular Biology. – Jan. 2013. – Vol. 101, iss. 1. – Р. 22.1.1–22.1.11.Bumgarner R. Overview of DNA microarrays: types, applications, and their future. Current Protocols in Molecular Biology, January 2013, vol. 101, iss. 1, рр. 22.1.1–22.1.11. https://doi.org/10.1002/0471142727.mb2201s101.Giganti, A. The actin cytoskeleton as a therapeutic target: state of the art and future directions / A. Giganti, E. Friederich // Progress in Cell Cycle Research. – 2003. – Vol. 5. – P. 511–525.Giganti A., Friederich E. The actin cytoskeleton as a therapeutic target: state of the art and future directions. Progress in Cell Cycle Research, 2003, vol. 5, pp. 511–525.Pollard, T. D. Actin and actin-binding proteins / T. D. Pollard // Cold Spring Harbor Perspectives in Biology. – Aug. 2016. – Vol. 8, no 8. – Р. a018226.Pollard T. D. Actin and actin-binding proteins. Cold Spring Harbor Perspectives in Biology, August 2016, vol. 8, no 8, р. a018226. https://doi.org/10.1101/cshperspect.a018226Kitamura, A. State-of-the-art fluorescence fluctuation-based spectroscopic techniques for the study of protein aggregation / A. Kitamura, M. Kinjo // Intern. J. of Molecular Sciences. – Mar. 2018. – Vol. 19, iss. 4. – Р. 964.Kitamura A., Kinjo M. State-of-the-art fluorescence fluctuation-based spectroscopic techniques for the study of protein aggregation. International Journal of Molecular Sciences, March 2018, vol. 19, iss. 4, р. 964. https://doi.org/10.3390/ijms19040964An integrative simulation model linking major biochemical reactions of actin-polymerization to structural properties of actin filaments / A. A. Halavatyi [et al.] // Biophysical Chemistry. – 2009 – Vol. 140. – P. 24–34.Halavatyi A. A., Nazarov P. V., Medves S., Van Troys M., Ampe C., ..., Friederich E. An integrative simulation model linking major biochemical reactions of actin-polymerization to structural properties of actin filaments. Biophysical Chemistry, 2009, vol. 140, pp. 24–34.Quantitative kinetic study of the actin-bundling protein l-plastin and of its impact on actin turn-over / Z. Al Tanoury [et al.] // Public Library of Science (PLoS) One. – 2010. – Vol. 5, no. 2. – Р. 669–677.Tanoury Z. Al, Schaffner-Reckinger E., Halavatyi A., Hoffmann C., Moes M., ..., Friederich E. Quantitative kinetic study of the actin-bundling protein l-plastin and of its impact on actin turn-over. Public Library of Science (PLoS) One, 2010, vol. 5, no. 2, pp. 669–677.Имитационная модель трехканальных люминесцентных изображений популяций раковых клеток / Е. В. Лисица [и др.] // Журнал прикладной спектроскопии. – 2014. – Т. 81, № 6. – С. 907–913.Lisitsa Y. V., Yatskou M. M., Apanasovich V. V., Apanasovich T. V., Shytsik M. M. Imitacionnaja model' trehkanal'nyh ljuminescentnyh izobrazhenij populjacij rakovyh kletok [Simulation model for three-channel luminescent images of cancer cell populations]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2014, vol. 81, no. 6, pp. 907–913 (in Russian).Алгоритм автоматической сегментации границ ядер раковых клеток на трехканальных люминесцентных изображениях / Е. В. Лисица [и др.] // Журнал прикладной спектроскопии. – 2015. – Т. 82, № 4. – С. 598–607Lisitsa Y. V., Yatskou M. M., Apanasovich V. V., Apanasovich T. V. Algoritm avtomaticheskoj segmentacii granic jader rakovyh kletok na trehkanal'nyh ljuminescentnyh izobrazhenijah [Algorithm for automatic segmentation of nuclear boundaries in cancer cells in three-channel luminescent images]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2015, vol. 82, no. 4, pp. 598–607 (in Russian).Яцков, Н. Н. Метод исследования флуоресценции молекулярных соединений с использованием алгоритмов интеллектуального анализа данных / Н. Н. Яцков, В. В. Скакун, В. В. Апанасович // Прикладные проблемы оптики, информатики, радиофизики и физики конденсированного состояния: материалы Пятой Междунар. науч.-практ. конф., Минск, 16–17 мая 2019 г. – Минск: Ин-т прикл. физ. проблем им. А. Н. Севченко БГУ, 2019. – С. 125–127.Yatskou M. M., Skakun V. V., Apanasovich V. V. Metod issledovaniya fluorescencii molekulyarnyh soedinenij s ispolzovaniem algoritmov intellektualnogo analiza dannyh [Method for investigating the fluorescence of molecular compounds using data mining algorithms]. Prikladnye problemy optiki, informatiki, radiofiziki i fiziki kondensirovannogo sostoyaniya: materialy Pyatoj Mezhdunarodnoj naucho-prakticheskoj konferencii, Minsk, 16–17 maya 2019 [Applied Problems of Optics, Informatics, Radiophysics and Condensed Matter Physics: Proceedings of the Fifth International Scientific and Practical Conference, Minsk, 16–17 May 2019]. Minsk, Inctitut priklladnyh fizicheskih problem im. A. N. Sevchenko Belorusskogo gosudarstvennogo universiteta, 2019, pp. 125–127 (in Russian).Яцков, Н. Н. Метод обработки кинетических кривых затухания флуоресценции с использованием алгоритмов интеллектуального анализа данных / Н. Н. Яцков, В. В. Скакун, В. В. Апанасович // Журнал прикладной спектроскопии. – 2020. – Т. 87, № 2. – С. 311–322.Yatskou M. M., Skakun V. V., Apanasovich V. V. Metod obrabotki kineticheskih krivyh zatuhanija fluorescencii s ispol'zovaniem algoritmov intellektual'nogo analiza dannyh [Method for processing fluorescence decay kinetic curves using data mining algorithms]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2020, vol. 87, no. 2, pp. 311–322 (in Russian).Электронные спектры и кинетика поляризации флуоресценции тонких пленок Zn-порфиринов / Н. Н. Яцков [и др.] // Журнал прикладной спектроскопии. – 2003. – Т. 70, № 3. – С. 335–339.Yatskov N. N., Apanasovich V. V., Koehorst R. B. M., van Hoek A., Schaafsma T. J. Jelektronnye spektry i kinetika poljarizacii fluorescencii tonkih plenok Zn-porfirinov [Electronic spectra and fluorescence polarization kinetics of thin Zn-porphyrine films]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2003, vol. 70, no. 3, pp. 335–339 (in Russian).Excitation energy migration in a photonic dye-zeolite antenna / M. Yatskou [et al.] // European J. of Chemical Physics and Physical Chemistry. – 2003. – Vol. 4(6). – P. 567–587.Yatskou M. M., Meyer M., Huber S., Pfenniger M., Calzaferri G. Excitation energy migration in a photonic dye-zeolite antenna. European Journal of Chemical Physics and Physical Chemistry, 2003, vol. 4, no. 6, рр. 567–587.Яцков, Н. Н. Метод комплексного анализа спектров флуктуации флуоресценции молекулярных соединений / Н. Н. Яцков, В. В. Скакун, В. В. Апанасович // Прикладные проблемы оптики, информатики, радиофизики и физики конденсированного состояния: материалы Пятой Междунар. науч.-практ. конф., Минск, 16–17 мая 2019 г. – Минск : Ин-т прикл. физ. проблем им. А. Н. Севченко БГУ, 2019. – С. 122–124.Yatskou M. M., Skakun V. V., Apanasovich V. V. Metod kompleksnogo analiza spektrov fluktuacii fluorescencii molekulyarnyh soedinenij [Method for complex analysis of fluorescence fluctuation spectra of molecular compounds]. Prikladnye problemy optiki, informatiki, radiofiziki i fiziki kondensirovannogo sostoyaniya: materialy Pyatoj Mezhdunarodnoj naucho-prakticheskoj konferencii, Minsk, 16–17 maya 2019 [Applied Problems of Optics, Informatics, Radiophysics and Condensed Matter Physics: Proceedings of the Fifth International Scientific and Practical Conference, Minsk, 16–17 May 2019]. Minsk, Inctitut priklladnyh fizicheskih problem im. A. N. Sevchenko Belorusskogo gosudarstvennogo universiteta, 2019, pp. 122–124 (in Russian).Комплексный анализ флуктуаций интенсивности флуоресценции молекулярных соединений / Н. Н. Яцков [и др.]. // Журнал прикладной спектроскопии. – 2020. – Т. 87, № 4. – С. 628–636.Yatskou M. M., Skakun V. V., Nederveen-Schippers L., Kortholt A., Apanasovich V. V. Kompleksnyj analiz fluktuacij intensivnosti fluorescencii molekuljarnyh soedinenij [Complex analysis of fluorescence intensity fluctuations of molecular compounds]. Zhurnal prikladnoj spektroskopii [Journal of Applied Spectroscopy], 2020, vol. 87, no. 4, pp. 628–636 (in Russian).

The authors declare that there are no conflicts of interest present.