Master
2024/2025
Machine Learning in Bioinformatics
Type:
Elective course (Data Analysis in Biology and Medicine)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
1 year, 1, 2 module
Mode of studies:
distance learning
Online hours:
14
Open to:
students of one campus
Master’s programme:
Data Analysis for Biology and Medicine
Language:
English
ECTS credits:
6
Course Syllabus
Abstract
The course introduces the theory and practice of machine learning algorithms and their applications in the area of bioinformatics. The students will learn data preprocessing techniques, methods of dimension reduction, technique of modeling using machine-learning algorithms, parameter tuning. The studied algorithms include linear regression with regularization (ridge regression, elastic net, lasso), multivariate adaptive regression splines, support vector machines, neural networks, k-nearest neighbors, classification and regression trees, random forest, gradient boosting. Workshops, which follow the lectures, seek to empower students with the practical skills in predictive modeling software tools, packages and applications. Many case studies of predictive models for bioinformatics data sets will be considered.
Learning Objectives
- To know the theory of the process and components of predictive modeling, types of predictive models, key steps of model creation, such as data-preprocessing, model construction and assessment of model performance
- To know various practical applications of predictive modeling using machine-learning algorithms for the databases of molecular biology
- To acquire the skills to use python functions from different python packages to apply different types of models such as linear and nonlinear regression models, linear and nonlinear classification models, regression trees and rule-based models
- To acquire the skills to use python functions from different python packages to pre-process the input data, i.e. calculate statistics, estimate skewness, apply appropriate transformation, perform PCA, find between-predictor correlations, generate dummy variables
- To acquire the skills to use python functions to measure predictor importance and model performance, use filtering methods, measure outcome error
- To apply the knowledge and tools of predictive analytics to bioinformatics applications
Expected Learning Outcomes
- apply the knowledge and tools of predictive analytics to real-life applications
- acquire the skills to implement machine-learning algorithms in python
- know the theory of machine-learning algorithms
Course Contents
- Big Data in Bioinformatics. Concepts of model building.
- Data Preprocessing.
- Linear regression models.
- Multivariate adaptive regression splines.
- Neural networks.
- Support vector machines. K-nearest neighbors.
- Measuring performance in classification models.
- Linear classification models
- Nonlinear classification models
- Decision Trees
- Machine-learning in bioinformatics
Interim Assessment
- 2024/2025 2nd module0.4 * Exam + 0.15 * Home assignment 1 + 0.15 * Home assignment 2 + 0.15 * Home assignment 3 + 0.15 * Home assignment 4
Bibliography
Recommended Core Bibliography
- Machine learning : a probabilistic perspective, Murphy, K. P., 2012
Recommended Additional Bibliography
- Data mining : practical machine learning tools and techniques, Witten, I. H., 2011
- Machine learning : the art and science of algorithms that make sense of data, Flach, P., 2014
- Witten, I. H. et al. Data Mining: Practical machine learning tools and techniques. – Morgan Kaufmann, 2017. – 654 pp.
- Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining : Practical Machine Learning Tools and Techniques (Vol. Fourth edition). Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1214611