Maria Poptsova

Associate Professor:Faculty of Computer Science / Big Data and Information Retrieval School
Laboratory Head:Faculty of Computer Science / Big Data and Information Retrieval School / Laboratory of Bioinformatics

Maria Poptsova has been at HSE since 2016.

Education and Degrees

2004
Candidate of Sciences* (PhD) in Biophysics
МГУ имени М.В. Ломоносова
Thesis Title: Transformation of autowaves in local inhomogeneous active media
1995
Degree in Physics
Lomonosov Moscow State University

* Candidate of Sciences
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

Professional Interests

bioinformatics genomics Comparative genomics machine learning application of machine learning methods to the analysis of biomedical data

Student Term / Thesis Papers

Bachelor
D. Petrachenka «Making Sense of Genomic Data with Machine Learning Methods». Faculty of Computer Science, 2018
L. Khotov «Recognition of 3' UTR Stem-Loop Structural Patterns in Alu Transposons in Human Genome». Faculty of Computer Science, 2018
Master
G. Maltsev «Building a Price Prediction Model Using Machine Learning Techniques». Faculty of Business and Management, 2018
T. Iakobidze «Machine Learning Approach to Reveal Functional Elements in Human Genome». Faculty of Business and Management, 2018
K. Cheloshkina «Application of Machine Learning Methods for Analysis of Genome Data». Faculty of Business and Management, 2018
E. Tevanyan «Regulation of Nucleosome Positioning by DNA Secondary Structures». Faculty of Computer Science, 2018
D. Attadjei «Prediction of Effects of Genetic Variants in Personalized Medicine». Faculty of Business and Management, 2018
S. Jin «Author Identification in Literature Texts Through Deep Learning». Faculty of Business and Management, 2018
D. Ibragimova «Finding Patterns in Epigenomics Data». Faculty of Business and Management, 2018
T. Suvorina «Data Mining in Marketing: Building Predictive Models for Customer Behavior Analysis, Scoring and Audience Segmentation». Faculty of Business and Management, 2018
N. Tsarkova «Machine Learning Applications to Genome Annotation Problem». Faculty of Business and Management, 2018
E. Levchuk «Search Engine Optimization Based on User Logs». Faculty of Business and Management, 2018
Y. Shahryary dizaji «Finding Patterns in Big Data». Faculty of Business and Management, 2017
C. Giri «Non Parametric Predictive Modeling of Large Scale Data». Faculty of Business and Management, 2017
N. Harale «Decision Support System for Finance Industry based on Big Data Stream Analytics». Faculty of Business and Management, 2017
A. Dallakyan «An Ensemble Approach in Predictive Modeling of Big Data». Faculty of Business and Management, 2017
A. Kuleshov «Analysis of Factors Influencing Engagement of a Reader into the Text». Faculty of Business and Management, 2017
N. Kazenova «Rental Listing Inquiries Classification (Kaggle Competitions Project)». Faculty of Business and Management, 2017

Full list of of student term / thesis papers

Courses (2018/2019)

Predictive Modeling
Applied Bioinformatics (Master’s programme; Faculty of Computer Science; programme "Data Analysis for Biology and Medicine"; 1 year, 3, 4 module)Rus
Modern Methods of Data Analysis (Master’s programme; Faculty of Computer Science; programme "Data Analysis for Biology and Medicine"; 1 year, 1, 2 module)Eng

Projects

DNA Punctuation

Publications¹²

Chapter Челошкина К. С., 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. (in press)
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. (in press)
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. (in press)
Chapter Tevanyan E., Poptsova M. Recognizing Patterns of Nucleosome and DNA Structures Positioning, in: Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine. Madrid, Spain : IEEE, 2018. 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
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).

Timetable for today

Full timetable

Dec

2018

"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.

Apr