Maria Poptsova
- Associate Professor:Faculty of Computer Science / Big Data and Information Retrieval School
- Maria Poptsova has been at HSE University since 2016.
Education and Degrees
- 2004
Candidate of Sciences* (PhD) in Biophysics
Lomonosov Moscow State University
Thesis Title: Transformation of autowaves in local inhomogeneous active media - 1995
Degree in Physics
Lomonosov Moscow State University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Continuing education / Professional retraining / Internships / Study abroad experience
1993 Amsterdam University, Netherlands
2005-2009 University of Connecticut, USA
2010-2011 Cornell University, USA
Student Term / Thesis Papers
- Bachelor
P. Iurlov, Recognition of Regions Containing Breakpoints in Cancer Genomes with Deep Learning Methods. Faculty of Computer Science, 2020
F. Pavlov, Recognition of DNA Secondary Structures as Nucleosome Barriers with Machine Learning Methods. Faculty of Computer Science, 2020
E. Iakovleva, Prediction of Triplex Structures by Deep Learning Methods. Faculty of Computer Science, 2020
P. Ilin, Detection of Coronary Atherosclerosis Variants on the Basis of Coronographic Video Sequences. Faculty of Computer Science, 2020
D. Sayfutdinova, Modeling of Predisposition of Cardio-Vascular Diseases in Different Ethnic Groups of Russia Based on Genetic Portraits. Faculty of Computer Science, 2020
M. Kharramov, Model of DNA-computing for Finding Solution of NP-complete Problem. Faculty of Mathematics, 2020
S. Burdanova, Recognition of Quadruplexes by Methods of Deep Learning in the Mouse Genome. Faculty of Computer Science, 2020
A. Kubaeva, Recognition of Patterns of Association of DNA Secondary Structures and Epigenetic Code by Machine-Learning Methods. Faculty of Computer Science, 2020
M. Bochkareva, H-DNA Areas Recognition Using Machine Learning Methods. Faculty of Computer Science, 2020
I. Balaban-irmenina, Recognition of G-quadruplexes by Methods of Deep Learning in the Saccharomyces cerevisiae Genome. Faculty of Computer Science, 2020
A. Voronkova, Comparative Analysis of Deep Learning Methods for the Tasks of Functional Genomic Element Recognition. Faculty of Computer Science, 2020
A. Chernitsov, Recognition of SINE 3'-Ends Throughout the Tree of Life. Faculty of Computer Science, 2019
E. Meshcheryakov, Methods of Machine Learning in Genetics and Genomics. Faculty of Computer Science, 2019
N. Beknazarov, Deep Neural Networks for Predicting the Functional Elements of the Genome. Faculty of Computer Science, 2019
P. Latyshev, Genome Annotation by Functional Elements by Methods of Supervised Learning. Faculty of Computer Science, 2019
A. Knyshov, Making Sense of Genomic Data With Machine Learning Methods. Faculty of Computer Science, 2019
L. Khotov, Recognition of 3' UTR Stem-Loop Structural Patterns in Alu Transposons in Human Genome. Faculty of Computer Science, 2018
D. Petrachenka, Making Sense of Genomic Data with Machine Learning Methods. Faculty of Computer Science, 2018
- Master
N. Konstantinovskiy, Generative Adversarial Network for DNA Secondary Structures Prediction. Graduate School of Business, 2020
S. Kharis, Analysis of the Pharmacological and Medical Factors Predicting Drug Safety. Faculty of Economic Sciences, 2020
A. Nostaeva, Search for Patterns of DNA Secondary Structures and Histone Modifications. Faculty of Computer Science, 2020
A. Shein, Transposon Recognition by Machine-Learning Methods. Graduate School of Business, 2019
E. Gatina, CNN Applications to Recognition of Genomic Sequences. Graduate School of Business, 2019
V. Verezubova, Prediction of Nucleosome Positioning Using Machine Learning Methods. Faculty of Computer Science, 2019
A. Petrov, Recognition of 3’-end RNA Secondary Structures in LINE-SINE Pairs of Transposons in Different Species Across the Entire Tree of Life. Faculty of Computer Science, 2019
U. Bykova, Recognition of Danio Rerio’s 3’-End Stem-Loops of SINE and LINE Transposons with Machine Learning Methods. Faculty of Computer Science, 2019
N. Tepliakova, Prediction of Functionality of DNA Secondary Structures with Deep Learning Methods. Faculty of Computer Science, 2019
N. Tsarkova, Machine Learning Applications to Genome Annotation Problem. Graduate School of Business, 2018
G. Maltsev, Building a Price Prediction Model Using Machine Learning Techniques. Graduate School of Business, 2018
K. Cheloshkina, Application of Machine Learning Methods for Analysis of Genome Data. Graduate School of Business, 2018
D. Attadjei, Prediction of Effects of Genetic Variants in Personalized Medicine. Graduate School of Business, 2018
D. Ibragimova, Finding Patterns in Epigenomics Data. Graduate School of Business, 2018
S. Jin, Author Identification in Literature Texts Through Deep Learning. Graduate School of Business, 2018
T. Iakobidze, Machine Learning Approach to Reveal Functional Elements in Human Genome. Graduate School of Business, 2018
E. Tevanyan, Regulation of Nucleosome Positioning by DNA Secondary Structures. Faculty of Computer Science, 2018
E. Levchuk, Search Engine Optimization Based on User Logs. Graduate School of Business, 2018
T. Suvorina, Data Mining in Marketing: Building Predictive Models for Customer Behavior Analysis, Scoring and Audience Segmentation. Graduate School of Business, 2018
N. Harale, Decision Support System for Finance Industry based on Big Data Stream Analytics. Graduate School of Business, 2017
Y. Shahryary dizaji, Finding Patterns in Big Data. Graduate School of Business, 2017
N. Kazenova, Rental Listing Inquiries Classification (Kaggle Competitions Project). Graduate School of Business, 2017
A. Kuleshov, Analysis of Factors Influencing Engagement of a Reader into the Text. Graduate School of Business, 2017
A. Dallakyan, An Ensemble Approach in Predictive Modeling of Big Data. Graduate School of Business, 2017
C. Giri, Non Parametric Predictive Modeling of Large Scale Data. Graduate School of Business, 2017
Courses (2020/2021)
Predictive Modeling
- Bioinformatics of DNA, RNA and Proteins (Minor; Faculty of Computer Science; 1, 2 module)Rus
- Medical bioinformatics (Minor; Faculty of Computer Science; 3, 4 module)Rus
- Modern Methods of Data Analysis (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
- Past Courses
Courses (2019/2020)
- Bioinformatics of DNA, RNA and Proteins (Minor; Faculty of Computer Science; 3, 4 module)Rus
- Elementary Genomics (Minor; Faculty of Computer Science; 1, 2 module)Rus
- Modern Methods of Data Analysis (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
Courses (2018/2019)
- Applied Bioinformatics (Master’s programme; Faculty of Computer Science; 1 year, 3, 4 module)Rus
- Modern Methods of Data Analysis (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
Publications23
- Chapter Cheloshkina K., Bzhikhatlov I., Poptsova M. Cancer Breakpoint Hotspots Versus Individual Breakpoints Prediction by Machine Learning Models, in: Proceedings 16th International Symposium, ISBRA 2020, Moscow, Russia, December 1–4, 2020. Lecture Notes in Computer Science Vol. 12304. Springer Publishing Company, 2020. doi P. 217-228. doi
- Article Beknazarov N., Jin S., Poptsova M. Deep learning approach for predicting functional Z-DNA regions using omics data // Scientific Reports. 2020. Vol. 10. P. 19134. doi
- Article Mirzaev K., Abdullaev S., Akmalova K., Sozaeva J., Grishina E., Shuev G., Bolieva L., Sozaeva M., Zhuchkova S., Gimaldinova N., Sidukova E., Serebrova S., Asoskova A., Шеин А. В., Poptsova M., Suleymanov S., Burashnikova I., Shikaleva A., Kachanova A., Fedorinov D., Sychev D. Interethnic differences in the prevalence of main cardiovascular pharmacogenetic biomarkers // Pharmacogenomics. 2020. Vol. 21. No. 10. P. 677-694. doi
- Article Cheloshkina K., Poptsova M. Understanding cancer breakpoint determinants with omics data // Integrative Cancer Science and Therapeutics. 2020. Vol. 7. P. 1-5. doi
- Chapter Tevanyan E., Poptsova M. Machine Learning Applications for Genomic Pattern Recognition Problem, in: Proceedings of the MACSPro Workshop 2019 / Ed. by Irina Lomazova, Anna Kalenkova, Р. Яворский. Vol. 2478: CEUR Workshop Proceedings. CEUR-WS.org, 2019. P. 139-148.
- Article Шеин А. В., Zaikin A., Poptsova M. Recognition of 3′-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models // Scientific Reports. 2019. Vol. 9. No. 7211. P. 1-16. doi
- Chapter Tevanyan E., Poptsova M. SEARCHING FOR NON-B-DNA STRUCTURES AS NUCLEOSOME BARRIERS WITH MACHINE LEARNING METHODS, in: The proceedings of International congress «Biotechnology: state of the art and perspectives» FEBRUARY 25 - 27, 2019. LLC “RED GROUP”, 2019. P. 363-364.
- Chapter Poptsova M., Шеин А. В., Zaikin A. SEQUENCE-BASED AND STRUCTURE-BASED MACHINE-LEARNING MODELS FOR RECOGNITION OF 3’-END L1 AND ALU STEM-LOOPS IN HUMAN GENOME, in: The proceedings of International congress «Biotechnology: state of the art and perspectives» FEBRUARY 25 - 27, 2019. LLC “RED GROUP”, 2019. P. 356-356.
- Article Cheloshkina K., Poptsova M. Tissue-specific impact of stem-loops and quadruplexes on cancer breakpoints formation // BMC Cancer. 2019. Vol. 19. No. 434. P. 1-17. doi
- Chapter Коновалов Д. Л., Попцова М. С. Роль CpG метилирования квадруплексов в эпигенетической регуляции. // В кн.: Сборник трудов 43-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2019". [б.и.], 2019. (in press)
- Chapter Cheloshkina K., Poptsova M. Machine-learning models for cancer breakpoints prediction based on DNA structure distributions, in: Сборник трудов 42-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2018". Институт проблем передачи информации им. А.А. Харкевича РАН, 2018. P. 1-5.
- Chapter Шеин А. В., Poptsova M. Recognition of 3’ UTR stem-loop in LINE transposons across the tree of life by machine learning methods, in: Сборник трудов 42-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2018". Институт проблем передачи информации им. А.А. Харкевича РАН, 2018. P. 1-5.
- Chapter Tevanyan E., Poptsova M. Recognizing Patterns of Nucleosome and DNA Structures Positioning, in: Сборник трудов 42-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2018". Институт проблем передачи информации им. А.А. Харкевича РАН, 2018. P. 1-10.
- Chapter Tevanyan E., Poptsova M. Recognizing Patterns of Nucleosome and DNA Structures Positioning, in: Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine. Madrid : IEEE, 2018. P. 2808-2809. doi
- Chapter Маткаримов О. О., Поливода Д. Э., Попцова М. С. Поиск паттернов ассоциации между функциональными элементами генома // В кн.: Доклады Международной конференции "Математическая биология и биоинформатика" / Под общ. ред.: В. Д. Лахно. Т. 7. Пущино : Государственное учреждение Институт математических проблем биологии РАН, 2018. Гл. 57. С. 1-3. doi
- Chapter Коновалов Д. Л., Попцова М. С. Построение модели машинного обучения, распознающей G-квадруплексы на основе физико-химических свойств ДНК // В кн.: Материалы Международного молодежного научного форума «ЛОМОНОСОВ-2018». М. : МАКС Пресс, 2018. (in press)
- Article Попцова М. С., Гречишникова Д. А. The Physical and Geometric Properties of Human Transposon Stem–Loop Structures under Natural Selection // Биофизика. 2017. Т. 62. № 6. С. 857-864. doi
- Article Гречишникова Д. А., Попцова М. С. Распознавание структур стебель-петля транспозонов человека и прогнозирование их функции при помощи модели машинного обучения. // Известия высших учебных заведений. Северо-Кавказский регион. Серия: Естественные науки. 2017. Т. 4. № 1. С. 63-69. doi
- Article Нечипуренко Ю., Ильичева И., Попцова М. С., Гроховский С. Физико-химические свойства ДНК в регуляторных участках генома // Актуальные вопросы биологической физики и химии. 2017. Т. 2. № 1. С. 339-340.
- Article Poptsova M., Grechishnikova D. Conserved 3' UTR stem-loop structure in L1 and Alu transposons in human genome: possible role in retrotransposition // BMC Genomics. 2016. Vol. 17. No. 1. P. 992. doi
- Chapter Попцова М. С., Гречишникова Д. DNAStructProfiler: автоматизированное построение профилей консервативности вторичных структур ДНК/РНК // В кн.: Сборник докладов VI Международной конференции "Математическая биология и биоинформатика" / Под общ. ред.: В. Д. Лахно. М. : Государственное учреждение Институт математических проблем биологии РАН, 2016. С. 134-135.
- Article Il'icheva I., Khodikov M., Nechipurenko D., Nechipurenko YD Y., Grokhovsky S., Poptsova M. Structural features of DNA that determine RNA polymerase II core promoter // BMC Genomics. 2016. Vol. 17. No. 1. P. 973. doi
- Chapter Попцова М. С., Заикин А. В. Отбор структур стебель-петля в промоторных областях транскрипционных факторов с помощью эволюционного алгоритма // В кн.: Сборник докладов VI Международной конференции "Математическая биология и биоинформатика" / Под общ. ред.: В. Д. Лахно. М. : Государственное учреждение Институт математических проблем биологии РАН, 2016.
Editorial board membership
2007: Guest Editor, Bioinformatics.
2007: Guest Editor, BMC Bioinformatics.
2007: Guest Editor, Nucleic Acids Research.
2007: Guest Editor, PLoS Computational Biology.
2007: Guest Editor, PLoS ONE (Plos One).
HSE Researchers Use Neural Networks to Study DNA
HSE scientists have proposed a way to improve the accuracy of finding Z-DNA, or DNA regions that are twisted to the left instead of to the right. To do this, they used neural networks and a dataset of more than 30,000 experiments conducted by different laboratories around the world. Details of the study are published in Scientific Reports.
Winners of the HSE International Laboratory Proposal Competition Announced
This year’s HSE competition for the creation of international laboratories which considered proposals for the period of January 1, 2021 – December 21, 2023, was conducted from June 15 to December 15, 2020.
Faculty of Computer Science Hosts First School on Machine Learning in Bioinformatics
Laboratory of Bioinformatics organised the first international summer school in bioinformatics. The school was held online on August 24-28.
Podcasts of the program "Physics and Lyrics", radio station "Mayak"
Head of the lab Maria Poptsova - guest on the radio station "Mayak"
HSE Showcases Innovations in Urbanism and Neural Networks at Russia’s Geek Picnic 2020
This year, Russia’s largest science and technology festival, Geek Picnic, was held online for the first time. Despite the new format, the festival programme included all of the event’s usual key features: expert lectures, workshops, competitions, and opportunities to socialize and network with fellow tech and science enthusiasts. HSE University once again served as the festival’s content partner.
GEEK PICNIC 2020
Head of the lab Maria Poptsova give a lecture at Geek Picnic 2020
Third Russian Winter School for Young Scientists and Doctors in Pharmacogenetics and Personalized Therapy
Head of the lab Maria Poptsova - participant and moderator of the round table at the Winter School of Young Scientists and Doctors on Pharmacogenetics and Personalized Therapy
Winter School 2020
Head of the lab Maria Poptsova give a lecture at the Computer Science Winter School for Graduate Students
Lecture hall "University Saturdays at HSE"
Head of the lab Maria Poptsova is a lecturer on University Saturdays
DNA Secondary Structures Lead to Gene Mutations that Increase the Risk of Cancer
Сancer Сells Will Become Vulnerable
"Worldwide Conversation on Women’s Higher Education and Equality in the Workplace"
On November 26, the HSE Faculty of Computer Science held the ‘IT Girls Night’ for the fifth time. This year the event was organized within the University of London’s campaign ‘Worldwide Conversation on Women’s Higher Education and Equality in the Workplace’. This campaign celebrates 150 years since the University of London opened up its ‘Special Examinations for Women’, the first university-level examinations offered for women in the UK. Ten years later, this step led to the University of London becoming the first institution of higher education in the UK to open up full degrees for women.
Results of Competition for Research and Teaching Laboratories Announced
This year, HSE has supported the founding of 10 new Research and Teaching Laboratories (RTLs) in various fields, from cognitive psychology and computer modeling, to international justice and economics of sports.
