Machine Learning in Bioinformatics

Learning from patterns in molecular biology data plays an important role in diagnosing disease, discovering new targets for therapy, and more generally in answering biological questions that lead to an improved understanding of biological systems with relevance to human health, industry, biotechnology, and agriculture.

This course focuses on methodology for the analysis of high-dimensional data in molecular biology, naturally addressing challenges that arise in the field such as learning from unlabelled data or from small numbers of samples. The methodology is introduced in the context of real-world applications with examples using real data.

Covered topics will include (a selection of) the following:

• Mixture models: (in)finite mixture models, EM algorithm, ECDF, bootstrapping.

• Clustering: k-means/medoids, density-based clustering, hierarchical clustering, clustering evaluation, choosing number of clusters.

• Statistical hypothesis testing: p-values, confidence intervals, multiple hypothesis testing, family-wise error rate, (local) false discovery rate.

• Testing using high-throughput count data: multifactorial designs, (generalized) linear models, analysis of variance, shrinkage estimation.

• Linear and non-linear dimensionality reduction: matrix decomposition (SVD), biplot representations, PCA, NMF, UMAP, autoencoders.

• Multivariate methods for heterogenous data: embeddings, multidimensional scaling (MDS), robust (non-metric) MDS, correspondence analysis, canonical correlation.

• Supervised learning: algorithms, performance metrics, curse of dimensionality, generalizability and model complexity, regularization, cross-validation, ML architectures for sequential data.

• Interpretability and explainability.

• Other selected advanced topics, including transfer learning self-supervised learning, semi-supervised learning, multimodal ML.

The final grade of the course consists of the following components:

• Individual Report: Written report about the bioinformatics project performed throughout the course. The project has a group part and an individual part. Students have to identify which tasks of the group project they were involved in, so that both parts are graded individually. If doubts arise about a student’s contribution, an oral interview can be conducted (weight 60%)

• Individual Presentation: individual presentation on the content of a book chapter or scientific paper (weight 20%)

• Individual Assignments: individual participation during the in-class discussions, and small assignments on the material (weight 20%)

The report grade itself consists of two parts, a group part (40% weight) and an individual part (60% weight).

Final grade calculation = 0.6 * Individual Report + 0.2 Individual Presentation + 0.2 * Individual Assignments

A passing final grade for the course can only be earned when at least a 5.0 is earned per component, for all components, and the weighted final grade is at least 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Individual Report: repair opportunity available only for the individual part.

• Individual Presentation: repair opportunity available.

• Individual Assignments: no repair.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).