Bioinformatics: Artifical Intelligence and Algorithmic Approaches (Modul: 13978)

Molecular biology is a scientific field which has undergone dramatic changes over the last four decades driven by novel scientific instruments such as sequencing machines allowing us to study genomes as well as gene activity. This has enabled a much deeper understanding of the molecular mechanism of life, evolution of species, and, very importantly, a better understanding of human disease.

A large part of the change was fueled by methods from Computer Science, most famously algorithms for comparing biological sequences (also known as approximate string matching) and for combining or assembling short sequences into complete genomes. As a matter of fact for large parts of bioinformatics the representation needed of biology is that of a string over the alphabet {A, C, G, T}.  

The focus on sequence analysis in the course allows to focus on state-of-the-art methods in bioinformatics while keeping the biological background quite abstract and thus easily accessible also to students with a limited natural sciences or biology background. 

The course will provide an introduction to modern bioinformatics and to selected applications from biology and medicine addressed with computational approaches based on classical algorithms and statistical machine learning, as well as modern deep learning approaches. The focus will be on four fundamental problem areas:

• Comparing sequences: Sequence alignment algorithms, Genome-scale approaches using index data structures, Alignment-free methods
• Analyzing gene expression: alignment-based and alignment-free methods to analyzing RNASeq, single-cell analysis
• Signals in sequences: identification of motifs, accessibility, and modification of DNA
• Sequence variations and relation to phenotypes: structural variants in disease, possibly pan-genome approaches

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/section.php?id=157834.

Seminar Artificial Intelligence for Drug Design (Modul: 13979)

Drug Design is one of the most exciting fields where Artificial Intelligence (AI) has a measurable positive impact on human lives by greatly accelerating the complicated and failure-prone process of designing new drugs. 

For this seminar we plan to focus on oligonucleotide and gene editing, two of the most modern approaches to treating disease.  

Some biological concepts (DNA sequences, genes, gene expression) are necessarily part of the research articles which are the topic of the seminar. We will introduce those in an initial presentation also including the processes targeted by disease.

However, the machine learning methods often use simple abstractions such as the representation of a DNA sequence as a string over the alphabet {A, C, G, T}.  In other words, no deep biological background is needed. Thus the seminar is accessible also to students with a limited natural sciences or biology background.

Students will present a topic based on—typically—one original research article chosen from a list of suggestions made available here and prepare a report on the same topic. Attending all talks by other students and participating in discussions is expected for a passing grade.

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/view.php?id=14656.

Seminar Computing at Scale in Machine Learning (Modul: 14445)

After successfully completing the module, students will be familiar with state-of-the-art problems and methodological approaches used in medical bioinformatics. They will have the ability to familiarize themselves with current research in medical bioinformatics from original research literature, to participte in a technical discussion within the context of international science and present scientific content in written and oral form.
Students will learn about specific state-of-the-art problems and methodological approaches used in medical bioinformatics. The applications will range from diagnostics and monitoring patients with sensor, clinical and omics data, to detect clinically relevant states or understand cellular processes relevant to diagnosis and disease as well mechanisms for treating diseases. Methods will include both algorithmic and machine learning approaches. 

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/view.php?id=14661.

Research Module in Artificial Intelligence: Specialization Medical Bioinformatics (Modul: 14060)

The research module helps students to prepare for a Master's thesis in the areas of medical bioinformatics, particularly with using Machine learning and AI methods. The format are weekly meetings in which students read an original research paper, prepare a project proposal and a project plan for a possible thesis project based on the original research paper after some preliminary analysis. The writing process, the formulation of scientific questions and the design to address them are important initial steps in the Master's process.

There will be weekly meetings, time and day of week by arrangement.

About the field:

Molecular biology and biomedicine are scientific fields which have undergone dramatic changes over the last four decades driven by novel scientific instruments such as sequencing machines allowing us to study genomes as well as gene activity. This has enabled a much deeper understanding of the molecular mechanism of life, evolution of species, and, very importantly, a better understanding of human disease.
A large part of the change was fueled by methods from Computer Science, most famously algorithms for comparing biological sequences (also known as approximate string matching) and for combining or assembling short sequences into complete genomes. As a matter of fact for large parts of bioinformatics the representation needed of biology is that of a string over the alphabet {A, C, G, T}.  

Consequently, by keeping the biological background quite abstract research on state-of-the-art methods in bioinformatics is easily accessible also to students with a limited natural sciences or biology background. 

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/view.php?id=14658.

Bioinformatics Laboratory (Modul: 14441)

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/view.php?id=14662.

Workflows for Machine Learning and Reproducible Science Laboratory (Modul: 14449)

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/view.php?id=14663.

Oberseminar Medizinische Bioinformatik

Detailed information for participants is available at https://www.b-tu.de/elearning/btu/course/section.php?id=157963.