Bioinformatics

• Gesamtes Lehrangebot der Bioinformatik

  E61aA1.1
  • 19000546 Mentoring
    Mentoring (Ulrike Seyferth)
    Schedule: Mo 14.04. 10:00-12:00, Mo 14.04. 10:00-12:00 (Class starts on: 2025-04-14)
    Location: A3/019 Seminarraum (Arnimallee 3-5)
    

    Comments

    The mentoring program offers events and counseling services primarily (but not only!) for first-year students. All offers are voluntary and are based on your needs and wishes!

    If you have questions or wishes, please contact us!

    Your mentors in mathematics, computer science and bioinformatics

  • 19211901 Lecture
    Computer-oriented Mathematics II (Robert Gruhlke)
    Schedule: Fr 12:00-14:00 (Class starts on: 2025-04-25)
    Location: T9/Gr. Hörsaal (Takustr. 9)
    

    Additional information / Pre-requisites

    Studierende der Mathematik (Monobachelor und Lehramt) und Bioinformatik, sowie Numerikinteressierte aus Physik, Informatik und anderen Natur- und Geisteswissenschaften.

    Comments

    Inhalt:

    Die Auswahl der behandelten numerischen Verfahren enthält Polynominterpolation, Newton-Cotes-Formeln zur numerische Integration und Euler-Verfahren für lineare Differentialgleichungen.

  • 19211902 Practice seminar
    Practice seminar for Computer-oriented Mathematics II (Robert Gruhlke)
    Schedule: Di 08:00-10:00, Di 16:00-18:00, Mi 16:00-18:00, Do 08:00-10:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-15)
    Location: A3/ 024 Seminarraum (Arnimallee 3-5)
    
  • 19211941 Zentralübung
    Practice seminar for Computer-oriented Mathematics II (Claudia Schillings)
    Schedule: Fr 14:00-16:00 (Class starts on: 2025-04-25)
    Location: A3/Hs 001 Hörsaal (Arnimallee 3-5)
    
  • 19234810 Proseminar
    Women in the History of Mathematics and Computer Science (Anina Mischau)
    Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
    Location: A6/SR 032 Seminarraum (Arnimallee 6)
    

    Additional information / Pre-requisites

    For mathematicians and computer scientists in a monobachelor's degree, creditable as ABV!

    Comments

    The seminar focuses on the development and rediscovery of the life stories and the work of some important mathematicians and computer scientists in the 19th and 20th centuries. The life and work of Sophie Germaine (1776-1831), Ada Lovelace (1815-1852), Sonja Kovalevskaya (1850-1891), Emmy Noether (1882-1935), Ruth Moufang (1905-1977), Grace Murray Hopper (1906-1992) and other female scientists are examined.

    The seminar is not about highlighting these women as an exception, because it would only set them on their exotic status. Rather, it is about a historical contextualization of their life and work. This not only enables an exemplary examination of social and cultural inclusion and exclusion processes along the gender category, but also the development of new perspectives on the traditional cultural history of both disciplines. The seminar is based on the approach of researching or discovering learning, i.e. the students will independently prepare and present individual seminar topics in group work. These presentations will then be discussed in the seminar. Through the use of observation sheets, a feedback culture is also to be tested that will be helpful in dealing with pupils and/or colleagues in later professional life.

  • 19300101 Lecture
    Algorithms and Data Structures (Wolfgang Mulzer)
    Schedule: Di 16:00-18:00, Fr 12:00-14:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-15)
    Location: Gr. Hörsaal (Raum B.001) (Arnimallee 22)
    

    Comments

    Qualification goals

    The students can analyze algorithms and data structures and their implementations with respect to running time, space requirements, and correctness. The students can describe different algorithms and data structures for typical applications and know how to use them in concrete settings. They can choose appropriate algorithms and data structures for a given task and are able to adapt them accordingly. Students can explain, identify and use different paradigms for designing new algorithms.

    Contents

    • abstract machine models
    • running time, correctness and space requirements
    • worst-case analysis
    • algorithms and randomness
    • algorithmic paradigms: divide and conquer, greedy, dynamic programming, exhaustive search
    • priority queues
    • ordered and unordered dictionaries (e.g., search trees, hash tables, skiplists)
    • algorithms for strings (string searching and radix trees)
    • graph algorithms 

    Suggested reading

    • P. Morin: Open Data Structures, an open content textboox.
    • T. H. Cormen, C. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms, MIT Press, 2022.
    • R. Sedgewick, K. Wayne: Algorithms, Addison-Wesley, 2011.
    • M. Dietzfelbinger, K. Mehlhorn, P. Sanders. Algorithmen und Datenstrukturen: Die Grundwerkzeuge, Springer, 2014.
    • J. Erickson. Algorithms, 2019
    • T. Roughgarden. Algorithms Illuminated. Cambridge University Press, 2022.

  • 19300102 Practice seminar
    Practice seminar for Algorithms and Data Structures (Wolfgang Mulzer)
    Schedule: Mo 14:00-16:00, Mo 16:00-18:00, Di 12:00-14:00, Mi 12:00-14:00, Mi 14:00-16:00, Mi 16:00-18:00, Do 16:00-18:00, Fr 14:00-16:00, Fr 16:00-18:00 (Class starts on: 2025-04-14)
    Location: T9/051 Seminarraum (Takustr. 9)
    
  • 19301001 Lecture
    Linear Algebra for Computer Science and Bioinformatics (Max Willert)
    Schedule: Mi 16:00-18:00, Do 10:00-12:00, Fr 10:00-12:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-17)
    Location: Hs 1b Hörsaal (Habelschwerdter Allee 45)
    

    Additional information / Pre-requisites

    The sign-up for the tutorial sessions will be announced in due time.

    Comments

    • linear algebra:
      • vector space, basis and dimension;
      • linear map, matrix and rank;
      • Gauss-elimination and linear systems of equations;
      • determinants, eigenvalues and eigenvectors;
      • euclidean vector spaces and orthonormalization;
      • principal component transformation;
      • Applications of linear algebra in affine geometry, statistics, and coding theory  (linear codes)

    Suggested reading

    • Klaus Jänich: Lineare Algebra, Springer-Lehrbuch, 10. Auflage 2004
    • Dirk Hachenberger: Mathematik für Informatiker, Pearson 2005
    • G. Grimmett, D. Welsh: Probability - An Introduction, Oxford Science Publications 1986
    • Kurt Meyberg, Peter Vachenauer: Höhere Mathematik 1, Springer-Verlag, 6. Auflage 2001
    • G. Berendt: Mathematik für Informatiker, Spektrum Akademischer Verlag 1994
    • Oliver Pretzel: Error-Correcting Codes and Finite Fields, Oxford Univ. Press 1996

  • 19301002 Practice seminar
    Practice seminar for Linear Algebra for Computer Science (Max Willert)
    Schedule: Mo 10:00-12:00, Mo 14:00-16:00, Mo 16:00-18:00, Di 08:00-10:00, Di 10:00-12:00, Di 12:00-14:00, Di 14:00-16:00, Di 16:00-18:00 (Class starts on: 2025-04-14)
    Location: T9/049 Seminarraum (Takustr. 9)
    
  • 19301501 Lecture
    Database Systems (Agnès Voisard)
    Schedule: Di 14:00-16:00, Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-15)
    Location: T9/Gr. Hörsaal (Takustr. 9)
    

    Additional information / Pre-requisites

    Requirements

    • ALP 1 - Functional Programming
    • ALP 2 - Object-oriented Programming
    • ALP 3 - Data structures and data abstractions
    • OR Informatik B

    Comments

    Content

    Database design with ERM/ERDD. Theoretical foundations of relational database systems: relational algebra, functional dependencies, normal forms. Relational database development: SQL data definitions, foreign keys and other integrity constraints, SQL as applicable language: essential language elements, embedding in programming language. Application programming; object-relational mapping. Security and protection concepts. Transaction subject, transactional guaranties, synchronization of multi user operations, fault tolerance features. Application and new developments: data warehousing, data mining, OLAP.

    Project: the topics are deepened in an implementation project for student groups.

    Suggested reading

    • Alfons Kemper, Andre Eickler: Datenbanksysteme - Eine Einführung, 5. Auflage, Oldenbourg 2004
    • R. Elmasri, S. Navathe: Grundlagen von Datenbanksystemen, Pearson Studium, 2005

  • 19301502 Practice seminar
    Practice seminar for Database systems (Muhammed-Ugur Karagülle)
    Schedule: Mo 12:00-14:00, Mo 14:00-16:00, Mo 16:00-18:00, Di 08:00-10:00, Di 10:00-12:00, Di 12:00-14:00, Mi 10:00-12:00, Mi 12:00-14:00, Mi 14:00-16:00, Do 08:00-10:00, Do 10:00-12:00, Do 12:00-14:00, Do 16:00-18:00, Fr 10:00-12:00, Fr 14:00-16:00, Fr 16:00-18:00 (Class starts on: 2025-04-14)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    
  • 19302613 Lab Seminar
    Planning, Realisation and Analysis of a Tutorial (Nicolas Perkowski)
    Schedule: Do 16:00-17:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-07)
    Location: T9/137 Konferenzraum (Takustr. 9)
    

    Additional information / Pre-requisites

    Time and location by appointment.

    Comments

    Content

    In a preparatory colloquium, current teaching methods and advising for tutors are presented and discussed. This colloquium is specifically for students, which particularly want to guide exercise sessions as a tutor for the mathematics and computer science students. An interview with the selection committee will take place even during the holidays, in which will be decided about the suitability as a tutor.

    After successful aptitude assessment a tutorial about a subjects of a lecture of the first four semesters bachelor study, should be prepare, expose, documented and analyzed.

    Suggested reading

    Seifert, J. W.: Visualisieren Präsentieren Moderieren. GABAL Verlag, 16. Auflage 2001

  • 19333611 Seminar
    Seminar: Deep Learning for biomedical applications (Vitaly Belik)
    Schedule: Mo 16:00-18:00 (Class starts on: 2025-04-14)
    Location: T9/053 Seminarraum (Takustr. 9)
    

    Comments

    Recent developments in the area of machine learning due to availability of data and computational power promise to revolutionize almost every area of science. The driving technology behind this advancement is deep learning – a machine learning technology based on artificial neural networks consisting of many layers. Deep learning is capable of processing huge amount of data of different nature and already outperforming humans in many decision-making tasks. Biomedical research became now a source of large heterogeneous data, i.e. images, video, activity sensors, omics and text data. Leveraging the opportunities of this deep learning technology in the biomedical field requires particular set of skills combining thorough knowledge of necessary algorithms, specifics of biomedical data and designated programming tools. In this course we aim to offer students with background in computer science an opportunity to acquire the above skills to be able to deploy deep learning technology with a focus on biomedical applications. The course is structured as a seminar, where students under extensive guidance of instructors read fundamental books and recent research articles on deep learning, learn necessary programming tools, and produce their own implementations of computational pipelines in case studies using already published or original data. Starting from fundamental aspects of deep learning we aim to cover its applications to e.g. image data, time series data, text data, complex networks.

    Suggested reading

    [1] Andresen N, Wöllhaf M, Hohlbaum K, Lewejohann L, Hellwich O, Thöne- Reineke C, Belik V, Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expres- sion analysis. Plos One, 15(4):e0228059, (2020) https://doi.org/10.1371/ journal.pone.0228059

    [2] Jarynowski A, Semenov A, Kamiński M, Belik V. Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning. J Med Internet Res 2021;23(11):e30529 https://doi.org//10.2196/30529

  • 19335011 Seminar
    Seminar: Networks, dynamic models and ML for data integration in the life sciences (Katharina Baum)
    Schedule: Di 12:00-14:00 (Class starts on: 2025-04-15)
    Location: T9/137 Konferenzraum (Takustr. 9)
    

    Comments

    Research seminar of the group Data Integration in the Life Sciences (DILiS). Also open for seminar participation in the Master's program, online participation possible. Please refer to the current schedule on the whiteboard!

    The seminar offers space for the discussion of advanced and integrative data analysis techniques, in particular presentations and discussion of ongoing or planned research projects, news from conferences, review and discussion of current literature and discussion of possible future teaching formats and content, and presentations, as well as final presentations on theses or project seminars. The seminar language is mostly English. Interested students are welcome to attend and drop in without obligation or present a topic of their own choice of interest to the working group as in a usual seminar. Please note: Individual dates may be canceled or postponed. Please contact me in case of questions (katharina.baum@fu-berlin.de)!

  • 19336717 Seminar / Undergraduate Course
    Active learning, uncertainty and XAI with applications in biomedicine (Katharina Baum)
    Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
    Location: T9/051 Seminarraum (Takustr. 9)
    

    Comments

    In this advanced seminar, we will discuss a variety of methods for machine learning. The focus will be on approaches to active learning, uncertainty estimation and its utilization, as well as methods for explaining models. The application and development of these methods for biomedical research questions will be explored using current research papers.

    Examples of approaches covered include:

    • selective sampling
    • SHAP values
    • Gaussian ensemble models
    • Bayesian neural networks

    The seminar will primarily be conducted in English, but of course, you are welcome to ask questions in German.

  • 19336901 Lecture
    Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

    The lecture on Advanced Data Visualization for Artificial Intelligence is a comprehensive exploration of state-of-the-art techniques and tools to create and validate complex visualizations for communicating data insights and stories, with a specific focus on applications in Natural Language Processing (NLP) and Explainable AI. The lecture will introduce participants to the nested model of visualization, which encompasses four layers: characterizing the task and data, abstracting into operations and data types, designing visual encoding and interaction techniques, and creating algorithms to execute techniques efficiently. This model will serve as a framework for designing and validating data visualizations.
    
    Furthermore, the lecture will delve into the application of data visualization in NLP, emphasizing the visualization of word embeddings and language models to aid in the exploration of semantic relationships between words and the interpretation of language model behavior. In the context of Explainable AI, the focus will be on using visualizations to explain model predictions and feature importance, thereby enhancing the interpretability of AI models. By leveraging the nested model of visualization and focusing on NLP and Explainable AI, the lecture aims to empower participants with the essential skills to design and validate advanced data visualizations tailored to these specific applications, ultimately enabling them to effectively communicate complex data patterns and gain deeper insights from their data.

  • 19336902 Practice seminar
    Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    
  • 19401513 Lab Seminar
    Software Project Management (SeqAn) (René Rahn)
    Schedule: Termine siehe LV-Details (Class starts on: 2025-03-10)
    Location: 1.1.53 Seminarraum E2 (Arnimallee 14)
    

    Additional information / Pre-requisites

    See German text version.

    Comments

    Content:

    In this internship algorithms for sequence analysis are implemented in the SeqAn software library, which is currently being developed in our research group. The contributions are graded on the basis of a written project report. The same module includes an accompanying seminar, which must also be attended by the participants of the internship. Target group:

    This course is aimed at students of bioinformatics. The internships are awarded in February via a special registration procedure via the Bioinformatics Study Office. Interested computer science students are treated subordinated.

    Requirements:

    Good knowledge in C/C++.

    Information on the software internship can be found on the Bioinformatics homepage.

    Suggested reading

    Literatur:

    Wird in der Vorbesprechung ausgegeben.

  • 19403413 Lab Seminar
    Software Project Management (Faster Bioinformatics with C++) (Chris Bielow)
    Schedule: Mo 13:00-14:00, Fr 10:00-12:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-03-24)
    Location: T9/K40 Multimediaraum (Takustr. 9)
    

    Additional information / Pre-requisites

    The distribution of places takes place every year in February.

    Comments

    See German description.

  • 19403613 Lab Seminar
    Machine Learning and AI in the Life Sciences: Methods and Applications (Tim Conrad, Christoph Tycowicz)
    Schedule: Fr 08:00-12:00 (Class starts on: 2025-04-25)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Additional information / Pre-requisites

    Prerequisites:

    Attended the Statistics course from the Master in Bioinformatics FU (or equivalent)

    Comments

    This course introduces key machine learning and AI techniques with a strong focus on their applications in the life sciences. You will learn how to analyze complex biomedical data, from pre-processing to model selection and interpretation, using techniques such as dimensionality reduction, feature selection, supervised and unsupervised learning, visual analysis, and deep learning.

    Through hands-on projects based on real-world datasets, you will gain practical experience in selecting and applying the right methods to tackle key challenges in the life sciences and bioinformatics, such as disease prediction, biomarker discovery, and patient (status) classification.  These projects will reinforce your learning while also helping you develop both technical expertise and scientific communication skills through presentations.

    By the end of the course, you will be able to process and model life science data, evaluate AI-driven insights, and effectively communicate your findings. Prior experience in programming (e.g., in R, Python, Java, or C/C++) is highly recommended.

  • 19406801 Lecture
    Algorithmic Bioinformatics II (Katharina Jahn, Martin Vingron)
    Schedule: Fr 08:00-10:00 (Class starts on: 2025-04-25)
    Location: T9/SR 005 Übungsraum (Takustr. 9)
    

    Comments

    In der Vorlesung werde folgende Inhalte behandelt: Multiples Sequenzalignment, formale Sprachen und HMMs, Motifsuche, Der FM-index, Algoirthmen zur RNA Analyse, Proteomics. In den Übungen werden die erarbeitenen Inhalte vertieft und Analyse- und Beweistechniken eingeübt.  

  • 19406802 Practice seminar
    Practice seminar for Algorithmic Bioinformatics II (Katharina Jahn, Martin Vingron)
    Schedule: Di 12:00-14:00, Di 16:00-18:00, Mi 10:00-12:00 (Class starts on: 2025-04-22)
    Location: , A3/SR 120, T9/055 Seminarraum, T9/SR 006 Seminarraum
    
  • 60100001 Lecture
    Statistics I for bioinformatics (Konrad Neumann)
    Schedule: Do 16:00-18:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-17)
    Location: A3/Hs 001 Hörsaal (Arnimallee 3-5)
    

    Additional information / Pre-requisites

     

     

                   

    Comments

    Content:

    see German desciption

                   

  • 60100002 Practice seminar
    Practice seminar for Statistics I for bioinformatics (Konrad Neumann)
    Schedule: Mi 16:00-18:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-16)
    Location: A7/SR 031 (Arnimallee 7)
    
  • 60101511 Seminar
    Seminar zu Projektmanagement im Softwarebereich (Rust-Programmierung) (Peter N. Robinson)
    Schedule: -
    Location: keine Angabe
    
  • 60101513 Lab Seminar
    Projektmanagement im Softwarebereich (Rust-Programmierung) (Peter N. Robinson)
    Schedule: -
    Location: keine Angabe
    

    Additional information / Pre-requisites

    Die Verteilung der Plätze erfolgt jedes Jahr im Februar.

    Comments

    See German description.

  • 60102501 Lecture
    Resampling techniques and their application (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 032 Seminarraum (Arnimallee 6)
    

    Comments

    In this course, we introduce resampling techniques for analyzing trials with small sample sizes. Special attention will be given to both estimation methods as well as inference procedures. We hereby will find answers to the questions (1) "How does resampling work?" and "When does resampling work"? Throughout the class we will study one sample, two samples and even factorial designs with independent and dependent observations. All algorithms will be presented and illustrated using R statistical software. Knowledge of fundamentals in statistical testing as well as basic skills in R are recommended and prerequisite. 

  • 60102502 Practice seminar
    Practice Seminar for Resampling techniques and their application (Frank Konietschke)
    Schedule: Mi 16:00-18:00 (Class starts on: 2025-04-16)
    Location: A6/SR 032 Seminarraum (Arnimallee 6)