Bioinformatics: Artifical Intelligence and Algorithmic Approaches (Modul 13978)

Learning Outcome
After successfully completing the module, students will have acquired an introduction to modern bioinformatics and to selected applications from biology and medicine. They understand the methodology through presentation of the central computational problems and an introduction of solutions based on classical algorithms and statistical machine learning, as well as modern deep learning approaches.

Contents
The focus will be on four fundamental problem areas:

• Comparing sequences: Sequence alignment algorithms, Genomescale 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, pan-genome approaches

Recommended Prerequisites

• Basic knowledge of probability and statistics, algorithms and data structures at the undergraduate level
• Introduction to machine learning at Master’s level
• Working knowledge of Python

Detailed information for participants is available at 
https://www.b-tu.de/elearning/btu/course/view.php?id=12912 

Artificial Intelligence for Drug Design (Modul 13979)

Learning Outcome
After successfully completing the module, students have insight into this exciting field of application for Artificial Intelligence (AI). They are able to acquire research literature and to present the topic orally as well as in a written report.

Contents
AI is revolutionizing drug design both for small molecule drugs - the prevalent drug modality - and novel modalities such as oligonucleotide therapeutics. Some of the progress has been achieved by transferring methods from established AI areas such as NLP. For other areas novel methodological developments were instrumental, with very exciting developments on the intersection between molecular dynamics and AI. The focus of the seminar will be on state-of-the-art methods and applications of AI in drug design for small molecule drugs and oligonucleotide therapeutics.

Recommended Prerequisites
Working knowledge of probability/statistics and modern machine learning methods.

Detailed information available for participants at 
https://www.b-tu.de/elearning/btu/course/view.php?id=12913

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

After successfully completing the module, students have an overview on how to solve large-scale computational problems in data science and machine learning. They know parallel approaches from multi-threaded computation on individual machines to implicit parallelism frameworks on compute clusters. They are familiar with algorithms and data structures supporting efficient exact or approximate (e.g. sketching) computation with massive data sets in and out of core. They are able to implement the algorithms. They can assess which methods can be used in a given situation.

The focus will be on the following areas: A review of memory-compute co-location and its impact on big data computations; Solving Machine Learning (ML) work loads using explicit parallelism, specifically multi-threaded computation on an individual machine; Introduction of implicit parallelism programming models as implemented for example in MapReduce, Spark and Ray and their application in ML; Sketching algorithms (e.g. CountMinSketch, HyperLogLog) or Bloom filters; Implementing ML methods using index data structures such as suffix or kd-trees.

Detailed information for participants is available at 
https://www.b-tu.de/elearning/btu/course/view.php?id=12914

Computing at Scale in Machine Learning: Distributed computing and algorithmic approaches (Modul: 14038)

Learning Outcomes:
Students will obtain an overview on how to solve large-scale computational problems in data science and machine learning using a) parallel approaches from multi-threaded computation on individual machines to implicit parallelism frameworks on compute clusters and b) algorithms and data structures supporting efficient exact or approximate computation with massive data sets in and out of core. In particular they will learn how to analyze relevant probabilistic data structures and algorithms and select and implement appropriate computational approaches for large-scale problems.

Contents:
The focus will be on the following areas:

• A review of memory-compute co-location and its impact on big data computations.
• Solving Machine Learning (ML) work loads using explicit parallelism, specifically multi-threaded computation on an individual machine.
• Introduction of implicit parallelism programming models as implemented for example in MapReduce, Spark and Ray and their application in ML.
• Probabilistic algorithms such as sketching algorithms (incl. CountMinSketch, HyperLogLog) or Bloom filters.
• Implementing ML methods using index data structures such as suffix or kd-trees.

Recommended Prerequisites:
Introduction to machine learning at Master’s level. Advanced knowledge of programming in Python and the Linux command line. 

Introduction to Bioinformatics (Modul: )

Learning Outcomes:
After successfully completing the module, students will have acquired an overview of the fundamentals of bioinformatics. This includes an introduction to relevant molecular processes, scientific instruments to investigate these processes, and the data generated by them. For central computational problems, students will be able to discuss advantages and disadvantages of statistical and basic algorithmic approaches, respectively adapt them to specific biological questions. Students will be able to analyze specific biological data using appropriate software libraries for Python.

Contents:
The focus will be on the basics of the following areas:

• An introduction to molecular biology including relevant scientific instruments and the Omics-data generated by them.
• Pair-wise and multiple sequence alignments, seed-and-extend approaches, and genome indexes
• Evolutionary models and phylogenetic trees
• Signals in sequences: identification of motifs
• Assembly of genomes and transcriptomes
• Gene expression analysis

Recommended Prerequisites:
Good knowledge of discrete probability, algorithms and data structures at the undergraduate level. Advanced knowledge of programming in Python and the Linux command line. 

Bioinformatics (Modul: 13866)

Research Module in Artificial Intelligence (Modul: 14060)

Appointment by arrangement in Sachsendorf

Oberseminar Medizinische Bioinformatik (Modul: 13600)

Appointment by arrangement in Sachsendorf