Bioinformatics

The novel measurement technologies in molecular biology has revolutionized life sciences and led to the emergence of data-driven, hypothesis-free research paradigm. The course introduces the informatics and statistical aspects of bioinformatics through key healthcare and pharmaceutical issues: medical decision support in diagnostic and therapy recommendation, the integrated analysis of genetic and genomic data, drug target prediction methods. The main data science concepts and methods demonstrated in this course are the following: Statistical inference paradigms. The multiple hypothesis testing problem. Methods for enrichment analysis. Methods of dimensionality reduction, with a primary focus on methods using expert knowledge in the form of ontologies. Clustering algorithms, especially methods which are applicable to multiple similarity matrices. High dimensionality prediction methods capable of handling heterogeneous representations, such as multiple kernel learning. Network theory, the structural properties of molecular interaction networks and network diffusion methods. Probabilistic graphical models and their inference and learning algorithms. Causal inference paradigms. Text mining methods in bioinformatics. Graph databases and semantic technologies (drug knowledge bases, gene ontologies, disease code systems). The theory will be demonstrated in the following real-world applications: Biomarker-based tumor diagnostics. The genetic background of common diseases. Co-occurrence and comorbidity networks. Examining the genetic background of healthy aging across multiple species. Drug target prediction Side effect and novel indication prediction of drugs and drug combinations.

The theory part of the course will be presented as a series of lectures, independent work is encouraged through homework assignments.

Antal Péter; - Arany Ádám - Bolgár Bence - Gézsi András - Hajós Gergely - Hullám Gábor -; Marx Péter - Millinghoffer András - Poppe László - Sárközy Péter:  Bioinformatics;  ISBN-13 978-963-2791-79-1, Typotex, 2014

Probability Theory

Requirements: 1. Lecturing interval: Submission and acceptance of the homework assignment by the end of the lecturing interval. 2. Examination interval: Oral examination, acceptance of the homework assignment is mandatory for oral examination. 3. Grading: The final grade is received on the oral examination. Additional possibilities: Homework assignments may be submitted by the end of the retake week.