Bachelor
2025/2026
Analysis of OmiX neurodata
Type:
Elective course (Cognitive Neurobiology)
When:
4 year, 1, 2 module
Open to:
students of one campus
Language:
English
Course Syllabus
Abstract
The course is designed for fourth-year undergraduate students studying cognitive neuroscience. The goal of the course is to provide students with knowledge and skills in analyzing multivariate biological data obtained using transcriptomics, proteomics, metabolomics and other OMICS technologies. The course covers topics such as data preprocessing, statistical analysis, machine learning and data visualization methods specific to neuroscience.
Learning Objectives
- The aim of the course is to provide theoretical foundations and practical skills in statistical analysis, machine learning and visualization for processing transcriptomic, proteomic and metabolomic data and their interpretation in neurobiological research.
Expected Learning Outcomes
- Demonstrate knowledge of the theoretical foundations of OMICS data analysis and their applications in neuroscience.
- Understand analysis methods specific to transcriptomics, proteomics and metabolomics.sing and analysis of large-scale biological datasets obtained by various OMICS technologies.
- Be proficient in statistical analysis and data visualization techniques applicable to OMICS research.
- Be able to pre-process and analyze large-scale biological datasets obtained using various OMICS technologies.
- Apply statistical analysis algorithms and machine learning fundamentals to OMICS data.
- Be able to visualize results for efficient communication and interpretation of complex biological data.
- Have experience in analyzing real OMICS datasets using appropriate software tools.
- Be able to present data and analysis results in the R/Python programming language.
- Able to critically evaluate and interpret the results of OMICS data analysis.
Course Contents
- Introduction to OMICs and Data Analysis Basics
- Specialized OMICs and Method Integration
Interim Assessment
- 2025/2026 2nd module0.1 * Activity in classes + 0.3 * Homework + 0.1 * Activity in classes + 0.3 * Homework + 0.1 * Test + 0.1 * Test
Bibliography
Recommended Core Bibliography
- Zhang, Y. (2007). Fundamentals of Biostatistics (6th ed.). Bernard Rosner. The American Statistician, 183. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.amstat.v61y2007mmayp183.183
- Кельберт, М. Я. Вероятность и статистика в примерах и задачах : учебное пособие / М. Я. Кельберт, Ю. М. Сухов. — Москва : МЦНМО, [б. г.]. — Том I : Основные понятия теории вероятностей и математической статистики — 2007. — 456 с. — ISBN 978-5-94057-253-4. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/9353 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
Recommended Additional Bibliography
- Байесовская статистика: Star Wars®, LEGO®, резиновые уточки и многое другое. - 978-5-4461-1655-3 - Курт Уилл - 2021 - Санкт-Петербург: Питер - https://ibooks.ru/bookshelf/377035 - 377035 - iBOOKS
- Зенков, А. В. Математическая статистика в задачах и упражнениях : учебное пособие / А. В. Зенков. - Москва ; Вологда : Инфра-Инженерия, 2022. - 108 с. - ISBN 978-5-9729-0866-0. - Текст : электронный. - URL: https://znanium.com/catalog/product/1902586
- Наглядная статистика. Используем R! : учебное пособие / А. Б. Шипунов, Е. М. Балдин, П. А. Волкова, А. И. Коробейников. — Москва : ДМК Пресс, 2014. — 298 с. — ISBN 978-5-94074-828-1. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/50572 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.