WiSe 24/25  
 Berlin Universi...  
 M.Sc. in Bioinf...  
 Course

BUA Joint Degree Programs

M.Sc. in Bioinformatics (2019 study regulations)

E81c

  • Foundations of Computer Science

    0262cA1.1
    • 19404901 Lecture
      Foundations in Computer Science (Knut Reinert)
      Schedule: Di 10:00-12:00 (Class starts on: 2024-10-15)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      In this lecture we will introduce concepts and methods of advanced algorithmics relevant to current reseach in bioinformatics. We will discuss Methods for the development and analysis of deterministic and randomised algorithms and foundations of compact data structures. Finally, the lecture will encompass concepts for parallel and vectorized computing. In more detail we will cover:

      - Introduction into different kinds of algorithms and analysis methods
      - Foundations of compact data structures 
      - Graph theiry and graph algorithms 
      - Analysis of randomized algorithms and data structures
      - Introduction into parallel and vectorized computing
      - Concepts, paradigms and frameworks for distributed computing
    • 19404902 Practice seminar
      Practice seminar: Foundations in Computer Science (Knut Reinert)
      Schedule: Di 12:00-14:00 (Class starts on: 2024-10-15)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

  • Foundations of Mathematics and Statistics

    0262cA1.2
    • 19405001 Lecture
      Foundations in Mathematics and Statistics (Max von Kleist, Liu-Wei Wang)
      Schedule: Do 10:00-12:00 (Class starts on: 2024-10-17)
      Location: A3/SR 120 (Arnimallee 3-5)

      Comments

      Goals: The students get a basic understanding of advanced mathematical concepts and methods in numerics, statistics, and optimization in the context of current research in bioinformatics and systems biology. They are able to choose problem-specific methods, to apply them in practice, and to evaluate the quality of the results.

      Contents: The course will address topics from the following areas:

      • Numerics
        • Modeling chemical reaction networks
        • Differential equation modeling
        • Parameter identification, sensitivity, identifiability
      • Optimization
        • Linear optimization (Simplex, polyhedra)
        • Integer linear optimization (branch-and-bound, branch-and-cut)
        • Local search and metaheuristics
      • Statistics
        • Testing and regression
        • Classification
        • Bootstrap and model evaluation
    • 19405002 Practice seminar
      Practice seminar for Foundations in Mathematics and Statistics (Max von Kleist, Liu-Wei Wang)
      Schedule: Do 12:00-14:00 (Class starts on: 2024-10-17)
      Location: A3/SR 120 (Arnimallee 3-5)

  • Foundations of Biomedicine

    0262cA1.3

  • Introduction to Focus Areas

    0262cA1.4
    • 19405152 RV
      Introduction to Focus Areas (Katharina Jahn, Knut Reinert, Max von Kleist)
      Schedule: Mo 10:00-12:00 (Class starts on: 2024-10-21)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

      Comments

      The module presents interdisciplinary exemplary problems and approaches from the three focus areas "Data Science for Bioinformatics", "Complex Systems in Bioinformatics" and "Advanced Algorithms in Bioinformatics". During a project, teams work together on concrete tasks on selected topics from these focus areas. They develop concrete proposals for solutions to practice-oriented problems, implement them and present the results.
    • 19405106 Seminar-style instruction
      SU: Introduction to Focus Areas (Katharina Jahn, Knut Reinert, Max von Kleist)
      Schedule: Mo 12:00-14:00 (Class starts on: 2024-10-21)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

  • Complex Systems in Biomedical Applications

    0262cB1.2
    • 60103101 Lecture
      Complex Systems in Biomedical Applications (Dorothee Günzel, Mathias Steinach)
      Schedule: Mo 14:00-16:00
      Location: Charité

      Comments

      Joint class taught by the Institute of Clinical Physiology and the Institute of Physiology at the Charité. The course will be split into two segments: the first seven appointments in the semester will take place at the Institute of Physiology, while the second seven appointments will take place at the Institute of Clinical Physiology.

      For further information: http://klinphys.charite.de/bioinfo/

      or mail to Dorothee Günzel

      Contents:

      Using selected, up-to-date examples from biology and physiology, the work steps from data acquisition, data processing, data preparation, data assessment to the modeling of complex physiological relationships are studied theoretically and practically. Models from the following areas are dealt with in more detail:

      - Basic biophysical and biochemical processes (e.g. free and facilitated diffusion through channel and transport proteins, active ion transport through membrane transporters, receptor-ligand interaction, interaction of structural and motor proteins)

      - Structure-function analysis of transport proteins

      - Biological networks (e.g. signal networks, metabolic networks, transportome models, feedback-mechanisms)

      - Modeling physiological functions of an organism (e.g. mass transfer to the kidney, blood- and immune-function, muscle movement, temperature regulation, circadian rhythm, cardiac- and circulatory function, autonomic regulation / heart-rate-variability, body-composition)
    • 60103102 Practice seminar
      Practice Seminar for Complex Systems in Biomedical Applications (Dorothee Günzel, Mathias Steinach)
      Schedule: Mo 16:00-18:00
      Location: Charité

  • Research Practical

    0262cB1.4
    • 19400432 Research Internship
      Bioinformatics Research Internship (Priyanka Banerjee, Tim Conrad, Dorothee Günzel, Katharina Jahn, Camila Mazzoni, Irmtraud Meyer, Frank Noe, Robert Preissner, Knut Reinert, Bernhard Renard, Martin Vingron, Max von Kleist, Jana Wolf)
      Schedule: -
      Location: keine Angabe

      Comments

      Please contact your advisor.

      Further information on our homepage.

  • Advanced Topics in Data Management

    0089cA1.29
    • 19304801 Lecture
      Geospatial Databases (Agnès Voisard)
      Schedule: Di 14:00-16:00 (Class starts on: 2024-10-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Zielgruppe:

      Studierende im Masterstudiengang Voraussetzungen: Vorlesung: Einf. in Datenbanksysteme

      Comments

      The goal of this course is to acquire the background of spatial databases, the kernel of Geographic Systems. The major aspects that will be handled are: modeling and querying geospatial information, spatial access methods (SAMs), data representation, basic operations (mostly from computational geometry), and optimization. Insights into current applications such as location-based services (e.g., navigation systems) will also be given. Knowledge in databases is necessary. This course encompasses: formal lectures, exercises, as well as a practical project with PostGIS.
       

      Suggested reading

      Handouts are enough to understand the course.

      The following book will be mostly used: P. Rigaux, M. Scholl, A. Voisard.Spatial Databases - With Application to GIS. Morgan Kaufmann, May 2001. 432 p. (copies in the main library)
    • 19304802 Practice seminar
      Practice seminar for Geospatial Databases (Agnès Voisard)
      Schedule: Do 14:00-16:00 (Class starts on: 2024-10-17)
      Location: T9/K 040 Multimediaraum (Takustr. 9)

  • Advanced Algorithms

    0089cA2.1
    • 19303501 Lecture
      Advanced Algorithms (László Kozma)
      Schedule: Di 10:00-12:00, Fr 10:00-12:00 (Class starts on: 2024-10-15)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Target audience

      All Master and Bachelor students who are interested in algorithms.

      Prerequisites

      Basic familiarity with the design and analysis of algorithms.

      Comments

      This course will focus on the design and analysis of algorithms, with topics including:

      • general principles of algorithm design,
      • randomized algorithms,
      • dynamic programming,
      • flow problems on graphs,
      • amortized analysis and advanced data structures,
      • theory of NP-completeness,
      • approximation methods for hard problems,
      • other topics.

      Prerequisites are basic knowledge of algorithms and relevant mathematics. All Bachelor and Master students interested in advanced algorithmic techniques are welcome. Lectures are in English.

      Suggested reading

      • Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 4th Ed. MIT Press 2022
      • Kleinberg, Tardos: Algorithm Design, Addison-Wesley 2005.
      • Sedgewick, Wayne: Algorithms, 4th Ed., Addison-Wesley 2016
    • 19303502 Practice seminar
      Practice seminar for Advanced Algorithms (László Kozma)
      Schedule: Fr 08:00-10:00, Fr 14:00-16:00 (Class starts on: 2024-10-18)
      Location: T9/046 Seminarraum (Takustr. 9)

  • Methods in Life Sciences

    0262cB3.16
    • 21697a Lecture
      Methods in Life Sciences (Mathias Wernet, Hochschullehrer*innen des FB Biologie, Chemie, Pharmazie)
      Schedule: Lecture: Friday, 15:00 - 16:30 h Seminar: Friday, 16:30 - 17:00 h (Class starts on: 2024-10-18)
      Location: Seminarraum 2 (R 034/IR) (Königin-Luise-Str. 12 / 16)

      Comments

      Current molecular and cell biological methods will be presented and discussed to enable students to relate algorithmic problems to specific biological questions. Koordination der LV: Florian Heyd: florian.heyd@fu-berlin.de Marco Preußner: mpreussner@zedat.fu-berlin.de

    • 21697b Seminar
      Methods in Life Sciences (Mathias Wernet, Hochschullehrer*innen des FB Biologie, Chemie, Pharmazie)
      Schedule: Lecture: Friday, 15:00 - 16:30 h Seminar: Friday, 16:30 - 17:00 h (Class starts on: 2024-04-19)
      Location: Königin-Luise-Str. 12-16, Seminarraum 2 (R 034/IR)

      Comments

      Current molecular and cell biological methods will be presented and discussed to enable students to relate algorithmic problems to specific biological questions. Koordination der LV: Florian Heyd: florian.heyd@fu-berlin.de Marco Preußner: mpreussner@zedat.fu-berlin.de

  • Methodology for Clinical Trials

    0262cD1.10
    • 60102001 Lecture
      Methods for clinical trials (N.N.)
      Schedule: Fr 14:00-16:00 (Class starts on: 2024-10-18)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.
    • 60102002 Practice seminar
      Practice seminar for Methods for clinical trials (N.N.)
      Schedule: Fr 16:00-18:00 (Class starts on: 2024-10-18)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

  • Advanced Biometrical Methods

    0262cD1.11
    • 60102301 Lecture
      Statistical Methods for Small Sample Sizes (Frank Konietschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2024-10-16)
      Location: A6/SR 031 Seminarraum (Arnimallee 6)

      Comments

      In this course, we introduce and discuss statistical inference methods for analyzing trials with small sample sizes. We hereby explore the impact of the standard assumption “N is large” and try to find an answer to the question “what means large?” The inference methods will cover estimation of treatment effects, confidence interval computations and hypothesis testing in both parametric and nonparametric models. Rank tests, bootstrap and permutation methods will be investigated in detail as approximation methods. This class aspires to learn about modern statistical tools that were designed to make accurate conclusions when sample sizes are rather small.

       
    • 60102302 Practice seminar
      Practice seminar for Statistical Methods for Small Sample Sizes (Frank Konietschke)
      Schedule: Mi 16:00-18:00 (Class starts on: 2024-10-16)
      Location: A6/SR 031 Seminarraum (Arnimallee 6)

  • Current Topics in Structural Bioinformatics

    0262cD1.21
    • 60101113 Lab Seminar
      Current questions of structural Bioinformatics (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

      Comments

      The practical course gives the student a chance to test his or her understanding of the material taught in the theory course. A short introduction on each topic will be given on this course for individual topics. The students will be assigned to a small project/task which they have to perform. The students will be assigned into groups of two/three/max four members, depending on the total number of students registered for the course.

      Website: https://bioinformatics.charite.de/main/course_practical.php

      Structure of the course:

      Everyday students will be assigned projects in groups for each topic. They work together to solve some interesting scientific problem. Each group will be closely supervised by the respective tutors. At the end of the lecture series, students (individual group) will be assigned one of the topic of the course, which they have to present in front of the wider audience. This will provide students, a way to practice presentation skills and can help them to develop the expertise needed to discuss their research in a clear and meaningful way.

      Learning how to answer specific questions and present data to a range of individuals, will help students in other endeavors, including future conference presentations, masters or dissertation defenses.

      Topic for practical lectures are:

      1. Introductory session
      2. Homology modeling
      3. 3D molecular superimposition of small molecules
      4. In silico screening, molecular fingerprints and chemical similarity
      5. Natural products and fragments based drug discovery
      6. In silico toxicity prediction
      7. Personalized medicine

      Important: Material from the theory course will be intensively used in the practical course. It is advisable, that the students need to attend the theory course before participating in the practical course. Both the courses are interlinked.

       

  • Current Topics in Cell Physiology

    0262cD1.4
    • 60100613 Lab Seminar
      Current topics in cell physiology (Dorothee Günzel)
      Schedule: -
      Location: keine Angabe

      Additional information / Pre-requisites

      Please bring lab coat, if available!

      Comments

      Block course during the semester break. Next available class: tba (two weeks, all day)

      Location: Charité Campus Benjamin Franklin (Steglitz, Hindenburgdamm 30), Institut für Klinische Physiologie

      For further information: http://klinphys.charite.de/bioinfo/

      or mail to Dorothee Günzel

      Within this course you will generate structural models of proteins by homology modelling. You will develop hypotheses which amino acids should be decisive for the structure.  These Hypotheses will be tested by carrying out molecular biologic experiments (such as site-directed mutagenesis by using two-step PCR). The construct will be cloned into expression vectors, transformed and amplified in bacteria, extracted, sequenced and overexpressed in cultured cells.

      These cells will be analyzed in the confocal laser scanning microscope and by other techniques. The results will be evaluated and interpreted in the context of the original hypitheses.

      The experimental part will be flanked by seminars introducing the theoretical background and the various techniques.

      The exact program of this course depends on the actual research of the institute and is tightly connected to our actual projects.

      Suggested reading

      Milatz S, Piontek J, Hempel C, Meoli L, Grohe C, Fromm A, Lee IM, El-Athman R, Günzel D (2017) Tight junction strand formation by claudin-10 isoforms and claudin-10a/-10b chimeras. Ann. N.Y. Acad. Sci. 1405: 102-115 (https://www.ncbi.nlm.nih.gov/pubmed/28633196)

      Piontek J, Winkler L, Wolburg H, Müller SL, Zuleger N, Piehl C, Wiesner B, Krause G, Blasig IE (2008) Formation of tight junction: determinants of homophilic interaction between classic claudins. FASEB J. 22: 146-158 (https://www.ncbi.nlm.nih.gov/pubmed/17761522)

       

  • Current Research Topics in Bioinformatics A

    0262cD2.1
    • 19333611 Seminar
      Seminar Deep Learning for biomedical applications (Vitaly Belik)
      Schedule: Mo 16:00-18:00 (Class starts on: 2024-10-14)
      Location: T9/051 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, Pauline Hiort, Pascal Iversen)
      Schedule: Fr 12:00-13:30 (Class starts on: 2024-07-26)
      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
      Graph-neural networks in the life sciences and beyond (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Di 12:00-14:00 (Class starts on: 2024-10-15)
      Location: A6/SR 009 Seminarraum (Arnimallee 6)

      Comments

      Complex data can often be naturally modeled as a graph. Graphs or networks describe the interaction between objects and are an effective tool to represent systems in many applications. Graph neural networks are neural networks that directly input graphs and have recently emerged as a powerful tool to analyze networks and to predict properties of nodes and connections.

      This seminar offers an in-depth exploration of Graph Neural Networks (GNNs) and their applications across various domains, with a particular emphasis on the life sciences and biomedicine. We will begin by discussing the fundamental concepts and architectures of GNNs, including graph convolutional networks (GCNs) and graph attention networks (GATs). Applications that are discussed include protein-protein interaction networks, drug discovery and personalized medicine. Students will read and present research papers and participate in critical discussions.

      The language of this seminar is planned to be English. The students are encouraged to present and discuss in English, but contributions in German are also possible.
    • 19402911 Seminar
      Journal Club Computational Biology (Knut Reinert)
      Schedule: Mo 14:00-16:00 (Class starts on: 2024-10-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Open for:

      Master and PhD students

      Comments

      Content:

      In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their research internship. Credits are only awarded for the presentation of papers.

      Please sign up on the Whiteboard (open "Site Browser" and look forJournal Club).

      Suggested reading

      aktuelle Publikationen aus der Forschung
    • 19404611 Seminar
      Open science, data handling and ethical aspects in bioinformatics (Thilo Muth)
      Schedule: Do 14:00-16:00 (Class starts on: 2024-10-17)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

      Planned topics are as follows:

      • Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
      • Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
      • Laboratory information management systems: sample management, integration of instruments and application, data exchange
      • Bioinformatic workflows: Galaxy, Snakemake and KNIME
      • Containerized frameworks: Advantages of using Docker, Bioconda etc.
      • Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
      • Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

      Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.
    • 19405911 Seminar
      Biochemical networks and disease (Jana Wolf)
      Schedule: Mi 12:00-14:00 (Class starts on: 2024-10-16)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations.
    • 19406411 Seminar
      Journal Club: Public Health Data Science (Max von Kleist)
      Schedule: Erster Termin: Mittwoch 23.Oct. Zeit: Mittwoch 10:00 (s.t.)-11:30 (Class starts on: 2024-10-23)
      Location: Online (https://rki.webex.com/rki/j.php?MTID=m032171b9d9eab45185ea259824052554 )

      Comments

      In this seminar, current research in the field of data-driven public health science, as well as the progress reports of PhD students and post-docs, will be presented. Master's students will present either an assigned journal article or their master's thesis, or they will report on their research internship. Credits will be awarded for article presentations only.

      Schedule: online, by arrangement
    • 19406611 Seminar
      Journal Club: Biomedical Data Science (Katharina Jahn)
      Schedule: Di 16:00-18:00 (Class starts on: 2024-10-15)
      Location: A3/SR 115 (Arnimallee 3-5)

      Comments

      In this seminar, we study current research publications in biomedical data science. Master students either present a research article, or their master thesis, or they present about their research internship. Credit points can only be earned for the presentation of research articles.

  • Current Research Topics in Bioinformatics B

    0262cD2.2
    • 19333611 Seminar
      Seminar Deep Learning for biomedical applications (Vitaly Belik)
      Schedule: Mo 16:00-18:00 (Class starts on: 2024-10-14)
      Location: T9/051 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, Pauline Hiort, Pascal Iversen)
      Schedule: Fr 12:00-13:30 (Class starts on: 2024-07-26)
      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
      Graph-neural networks in the life sciences and beyond (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Di 12:00-14:00 (Class starts on: 2024-10-15)
      Location: A6/SR 009 Seminarraum (Arnimallee 6)

      Comments

      Complex data can often be naturally modeled as a graph. Graphs or networks describe the interaction between objects and are an effective tool to represent systems in many applications. Graph neural networks are neural networks that directly input graphs and have recently emerged as a powerful tool to analyze networks and to predict properties of nodes and connections.

      This seminar offers an in-depth exploration of Graph Neural Networks (GNNs) and their applications across various domains, with a particular emphasis on the life sciences and biomedicine. We will begin by discussing the fundamental concepts and architectures of GNNs, including graph convolutional networks (GCNs) and graph attention networks (GATs). Applications that are discussed include protein-protein interaction networks, drug discovery and personalized medicine. Students will read and present research papers and participate in critical discussions.

      The language of this seminar is planned to be English. The students are encouraged to present and discuss in English, but contributions in German are also possible.
    • 19402911 Seminar
      Journal Club Computational Biology (Knut Reinert)
      Schedule: Mo 14:00-16:00 (Class starts on: 2024-10-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Open for:

      Master and PhD students

      Comments

      Content:

      In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their research internship. Credits are only awarded for the presentation of papers.

      Please sign up on the Whiteboard (open "Site Browser" and look forJournal Club).

      Suggested reading

      aktuelle Publikationen aus der Forschung
    • 19404611 Seminar
      Open science, data handling and ethical aspects in bioinformatics (Thilo Muth)
      Schedule: Do 14:00-16:00 (Class starts on: 2024-10-17)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

      Planned topics are as follows:

      • Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
      • Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
      • Laboratory information management systems: sample management, integration of instruments and application, data exchange
      • Bioinformatic workflows: Galaxy, Snakemake and KNIME
      • Containerized frameworks: Advantages of using Docker, Bioconda etc.
      • Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
      • Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

      Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.
    • 19405911 Seminar
      Biochemical networks and disease (Jana Wolf)
      Schedule: Mi 12:00-14:00 (Class starts on: 2024-10-16)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations.
    • 19406411 Seminar
      Journal Club: Public Health Data Science (Max von Kleist)
      Schedule: Erster Termin: Mittwoch 23.Oct. Zeit: Mittwoch 10:00 (s.t.)-11:30 (Class starts on: 2024-10-23)
      Location: Online (https://rki.webex.com/rki/j.php?MTID=m032171b9d9eab45185ea259824052554 )

      Comments

      In this seminar, current research in the field of data-driven public health science, as well as the progress reports of PhD students and post-docs, will be presented. Master's students will present either an assigned journal article or their master's thesis, or they will report on their research internship. Credits will be awarded for article presentations only.

      Schedule: online, by arrangement
    • 19406611 Seminar
      Journal Club: Biomedical Data Science (Katharina Jahn)
      Schedule: Di 16:00-18:00 (Class starts on: 2024-10-15)
      Location: A3/SR 115 (Arnimallee 3-5)

      Comments

      In this seminar, we study current research publications in biomedical data science. Master students either present a research article, or their master thesis, or they present about their research internship. Credit points can only be earned for the presentation of research articles.

  • Current Research Topics in Bioinformatics C

    0262cD2.3
    • 19333611 Seminar
      Seminar Deep Learning for biomedical applications (Vitaly Belik)
      Schedule: Mo 16:00-18:00 (Class starts on: 2024-10-14)
      Location: T9/051 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, Pauline Hiort, Pascal Iversen)
      Schedule: Fr 12:00-13:30 (Class starts on: 2024-07-26)
      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
      Graph-neural networks in the life sciences and beyond (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Di 12:00-14:00 (Class starts on: 2024-10-15)
      Location: A6/SR 009 Seminarraum (Arnimallee 6)

      Comments

      Complex data can often be naturally modeled as a graph. Graphs or networks describe the interaction between objects and are an effective tool to represent systems in many applications. Graph neural networks are neural networks that directly input graphs and have recently emerged as a powerful tool to analyze networks and to predict properties of nodes and connections.

      This seminar offers an in-depth exploration of Graph Neural Networks (GNNs) and their applications across various domains, with a particular emphasis on the life sciences and biomedicine. We will begin by discussing the fundamental concepts and architectures of GNNs, including graph convolutional networks (GCNs) and graph attention networks (GATs). Applications that are discussed include protein-protein interaction networks, drug discovery and personalized medicine. Students will read and present research papers and participate in critical discussions.

      The language of this seminar is planned to be English. The students are encouraged to present and discuss in English, but contributions in German are also possible.
    • 19402911 Seminar
      Journal Club Computational Biology (Knut Reinert)
      Schedule: Mo 14:00-16:00 (Class starts on: 2024-10-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Open for:

      Master and PhD students

      Comments

      Content:

      In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their research internship. Credits are only awarded for the presentation of papers.

      Please sign up on the Whiteboard (open "Site Browser" and look forJournal Club).

      Suggested reading

      aktuelle Publikationen aus der Forschung
    • 19404611 Seminar
      Open science, data handling and ethical aspects in bioinformatics (Thilo Muth)
      Schedule: Do 14:00-16:00 (Class starts on: 2024-10-17)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

      Planned topics are as follows:

      • Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
      • Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
      • Laboratory information management systems: sample management, integration of instruments and application, data exchange
      • Bioinformatic workflows: Galaxy, Snakemake and KNIME
      • Containerized frameworks: Advantages of using Docker, Bioconda etc.
      • Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
      • Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

      Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.
    • 19405911 Seminar
      Biochemical networks and disease (Jana Wolf)
      Schedule: Mi 12:00-14:00 (Class starts on: 2024-10-16)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations.
    • 19406411 Seminar
      Journal Club: Public Health Data Science (Max von Kleist)
      Schedule: Erster Termin: Mittwoch 23.Oct. Zeit: Mittwoch 10:00 (s.t.)-11:30 (Class starts on: 2024-10-23)
      Location: Online (https://rki.webex.com/rki/j.php?MTID=m032171b9d9eab45185ea259824052554 )

      Comments

      In this seminar, current research in the field of data-driven public health science, as well as the progress reports of PhD students and post-docs, will be presented. Master's students will present either an assigned journal article or their master's thesis, or they will report on their research internship. Credits will be awarded for article presentations only.

      Schedule: online, by arrangement
    • 19406611 Seminar
      Journal Club: Biomedical Data Science (Katharina Jahn)
      Schedule: Di 16:00-18:00 (Class starts on: 2024-10-15)
      Location: A3/SR 115 (Arnimallee 3-5)

      Comments

      In this seminar, we study current research publications in biomedical data science. Master students either present a research article, or their master thesis, or they present about their research internship. Credit points can only be earned for the presentation of research articles.

  • Special Aspects of Bioinformatics A

    0262cD2.4
    • 19328301 Lecture Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe

      Additional information / Pre-requisites

      https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

      Comments

      The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

      This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

      1. Be able to select and apply methods for designing visualizations based on a problem,
      2. know essential theoretical basics of visualization for graphical perception and cognition,
      3. know and be able to select visualization approaches and their advantages and disadvantages,
      4. be able to evaluate visualization solutions critically, and
      5. have acquired practical skills for implementing visualizations.

      This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

      In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

      Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

      Suggested reading

      Textbuch

      Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

       

      Zusätzliche Literatur

      Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

      Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

      Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
    • 19336801 Lecture
      Integrative analysis and including prior knowledge for data in the life sciences (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Mi 10:00-12:00 (Class starts on: 2024-10-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

      Comments

      Especially in the life sciences, data of different origins are often available for a question, and researchers already have prior knowledge, for example on dynamic aspects, or on spatial or regulatory relationships between entities. This course deals with analysis methods that can combine different data and prior knowledge. For example, we discuss how to link continuous and categorical data in mixed models, but also network integration, or multi-factorial matrix multiplication. A focus topic will deal with various approaches to informed machine learning such as graph-neural networks, transfer learning or current research methods such as simulation-based pre-training. The focus here is explicitly not on the processing of images, but on tabular or other data types. This course will be offered in English.
    • 60103201 Lecture
      Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

      Comments

      Course overview:

      Website: https://bioinformatics.charite.de/main/course_theoretical.php

      The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

      Structure of the course:

      This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

      Topics for lectures are:

      • 1. Introductory session
      • 2. Peptide design
      • 3. Homology modeling
      • 4. 3D Molecular superimposition of small molecules
      • 5. In silico screening, molecular fingerprints and chemical similarity
      • 6. Molecular docking
      • 7. Natural products and fragments based drug discovery
      • 8. In silico toxicity prediction
    • 19328302 Practice seminar Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe
    • 19336802 Practice seminar
      Integrative analysis of biomedical data tutorials (Katharina Baum)
      Schedule: Fr 10:00-12:00 (Class starts on: 2024-10-18)
      Location: T9/046 Seminarraum (Takustr. 9)
    • 60103202 Practice seminar
      Practice seminar for Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

  • Special Aspects of Bioinformatics B

    0262cD2.5
    • 19328301 Lecture Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe

      Additional information / Pre-requisites

      https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

      Comments

      The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

      This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

      1. Be able to select and apply methods for designing visualizations based on a problem,
      2. know essential theoretical basics of visualization for graphical perception and cognition,
      3. know and be able to select visualization approaches and their advantages and disadvantages,
      4. be able to evaluate visualization solutions critically, and
      5. have acquired practical skills for implementing visualizations.

      This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

      In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

      Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

      Suggested reading

      Textbuch

      Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

       

      Zusätzliche Literatur

      Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

      Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

      Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
    • 19336801 Lecture
      Integrative analysis and including prior knowledge for data in the life sciences (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Mi 10:00-12:00 (Class starts on: 2024-10-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

      Comments

      Especially in the life sciences, data of different origins are often available for a question, and researchers already have prior knowledge, for example on dynamic aspects, or on spatial or regulatory relationships between entities. This course deals with analysis methods that can combine different data and prior knowledge. For example, we discuss how to link continuous and categorical data in mixed models, but also network integration, or multi-factorial matrix multiplication. A focus topic will deal with various approaches to informed machine learning such as graph-neural networks, transfer learning or current research methods such as simulation-based pre-training. The focus here is explicitly not on the processing of images, but on tabular or other data types. This course will be offered in English.
    • 60103201 Lecture
      Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

      Comments

      Course overview:

      Website: https://bioinformatics.charite.de/main/course_theoretical.php

      The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

      Structure of the course:

      This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

      Topics for lectures are:

      • 1. Introductory session
      • 2. Peptide design
      • 3. Homology modeling
      • 4. 3D Molecular superimposition of small molecules
      • 5. In silico screening, molecular fingerprints and chemical similarity
      • 6. Molecular docking
      • 7. Natural products and fragments based drug discovery
      • 8. In silico toxicity prediction
    • 19328302 Practice seminar Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe
    • 19336802 Practice seminar
      Integrative analysis of biomedical data tutorials (Katharina Baum)
      Schedule: Fr 10:00-12:00 (Class starts on: 2024-10-18)
      Location: T9/046 Seminarraum (Takustr. 9)
    • 60103202 Practice seminar
      Practice seminar for Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

  • Special Aspects of Bioinformatics C

    0262cD2.6
    • 19328301 Lecture Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe

      Additional information / Pre-requisites

      https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

      Comments

      The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

      This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

      1. Be able to select and apply methods for designing visualizations based on a problem,
      2. know essential theoretical basics of visualization for graphical perception and cognition,
      3. know and be able to select visualization approaches and their advantages and disadvantages,
      4. be able to evaluate visualization solutions critically, and
      5. have acquired practical skills for implementing visualizations.

      This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

      In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

      Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

      Suggested reading

      Textbuch

      Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

       

      Zusätzliche Literatur

      Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

      Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

      Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
    • 19336801 Lecture
      Integrative analysis and including prior knowledge for data in the life sciences (Katharina Baum, Pauline Hiort, Pascal Iversen)
      Schedule: Mi 10:00-12:00 (Class starts on: 2024-10-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

      Comments

      Especially in the life sciences, data of different origins are often available for a question, and researchers already have prior knowledge, for example on dynamic aspects, or on spatial or regulatory relationships between entities. This course deals with analysis methods that can combine different data and prior knowledge. For example, we discuss how to link continuous and categorical data in mixed models, but also network integration, or multi-factorial matrix multiplication. A focus topic will deal with various approaches to informed machine learning such as graph-neural networks, transfer learning or current research methods such as simulation-based pre-training. The focus here is explicitly not on the processing of images, but on tabular or other data types. This course will be offered in English.
    • 60103201 Lecture
      Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

      Comments

      Course overview:

      Website: https://bioinformatics.charite.de/main/course_theoretical.php

      The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

      Structure of the course:

      This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

      Topics for lectures are:

      • 1. Introductory session
      • 2. Peptide design
      • 3. Homology modeling
      • 4. 3D Molecular superimposition of small molecules
      • 5. In silico screening, molecular fingerprints and chemical similarity
      • 6. Molecular docking
      • 7. Natural products and fragments based drug discovery
      • 8. In silico toxicity prediction
    • 19328302 Practice seminar Cancelled
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe
    • 19336802 Practice seminar
      Integrative analysis of biomedical data tutorials (Katharina Baum)
      Schedule: Fr 10:00-12:00 (Class starts on: 2024-10-18)
      Location: T9/046 Seminarraum (Takustr. 9)
    • 60103202 Practice seminar
      Practice seminar for Structural Bioinformatics Methods in Drug Development (Robert Preissner, Priyanka Banerjee)
      Schedule: -
      Location: keine Angabe

  • Selected Topics in Bioinformatics A

    0262cD2.7
    • 216221a Lecture
      Methods for investigating the RNA structurome and RNA-RNA interactome (Irmtraud Meyer)
      Schedule: block course: 24.02. - 28.02.25 (Mo to Fr), 10.03. - 14.03.25 (Mo to Fr) and 17.03. - 18.03.2025; (Mo and Tu); all day
      Location: Berlin Institute for Medical Systems Biology (BIMSB); Max Delbrück Center for Molecular Medicine in the Helmholtz Association Hannoversche Str. 28, 10115 Berlin, room 2.04 (seminar room)

      Information for students

      This module comprises lectures (216221a), exercises (216221b) and a seminar (216221c) which have to be booked in conjunction. The registrations for this module opens at the start of the winter term for one month (and is NOT part of the tombola system). Please be sure to register twice (!), both via Campus Management and via Whiteboard. More information on the course is listed on the corresponding course web-page on Whiteboard.
    • 216221b Practice seminar
      Methods for investigating the RNA structurome and RNA-RNA interactome (Irmtraud Meyer)
      Schedule: block course: 24.02. - 28.02.25 (Mo to Fr), 10.03. - 14.03.25 (Mo to Fr) and 17.03. - 18.03.2025; (Mo and Tu); all day
      Location: Berlin Institute for Medical Systems Biology (BIMSB); Max Delbrück Center for Molecular Medicine in the Helmholtz Association Hannoversche Str. 28, 10115 Berlin, room 2.04

      Information for students

      This module comprises lectures (216221a), exercises (216221b) and a seminar (216221c) which have to be booked in conjunction. The registrations for this module opens at the start of the winter term for one month (and is NOT part of the tombola system). Please be sure to register twice (!), both via Campus Management and via Whiteboard. More information on the course is listed on the corresponding course web-page on Whiteboard.
    • 216221c Seminar
      Methods for investigating the RNA structurome and RNA-RNA interactome (Irmtraud Meyer)
      Schedule: block course: 24.02. - 28.02.25 (Mo to Fr), 10.03. - 14.03.25 (Mo to Fr) and 17.03. - 18.03.2025; (Mo and Tu); all day
      Location: Berlin Institute for Medical Systems Biology (BIMSB); Max Delbrück Center for Molecular Medicine in the Helmholtz Association Hannoversche Str. 28, 10115 Berlin, room 2.04

      Information for students

      This module comprises lectures (216221a), exercises (216221b) and a seminar (216221c) which have to be booked in conjunction. The registrations for this module opens at the start of the winter term for one month (and is NOT part of the tombola system). Please be sure to register twice (!), both via Campus Management and via Whiteboard. More information on the course is listed on the corresponding course web-page on Whiteboard.

  • Modules w/o courses in this semester

    53 Modules
    • Complex Systems in Bioinformatics 0262cB1.1
    • Computer-Aided Drug Design 0262cB1.10
    • Current topics in cell-physiology 0262cB1.11
    • Computational Systems Biology 0262cB1.12
    • Ethics and Policy Questions 0262cB1.13
    • Ethics and Policy Questions 0262cB1.3
    • Current research topics in Complex Systems 0262cB1.5
    • Advanced Network Analysis 0262cB1.6
    • Human Evolution 0262cB1.7
    • Special aspects of Complex Systems 0262cB1.8
    • Selected topics in Complex Systems 0262cB1.9
    • Network-Based Information Systems 0089cA1.13
    • Distributed Systems 0089cA1.20
    • Special aspects of Data Science in the Life Sciences 0262cB2.10
    • Selected topics in Data Science in the Life Sciences 0262cB2.11
    • Current topics in medical genomics 0262cB2.12
    • Machine Learning in Bioinformatics 0262cB2.13
    • Advanced Biometrical Methods 0262cB2.18
    • Applied Machine Learning in Bioinformatics 0262cB2.19
    • Medical Bioinformatics 0262cB2.4
    • Current research topics in Data Science in Life Sciences 0262cB2.5
    • Machine Learning in Bioinformatics 0262cB2.6
    • Big Data Analysis in Bioinformatics 0262cB2.7
    • Complex Data Analysis in Physiology 0262cB2.8
    • Methodology for clinical trials 0262cB2.9
    • Data Science in the Life Sciences 0590aB2.1
    • Data Science in the Life Sciences 0590bB1.1
    • Advanced Algorithms for Bioinformatics 0262cB3.1
    • Applied Sequence Analysis 0262cB3.10
    • Environmental metagenomics 0262cB3.11
    • Current topics in structural bioinformatics 0262cB3.15
    • Methods in Life Sciences 0262cB3.2
    • Biodiversity and Evolution 0262cB3.5
    • Structural Bioinformatics 0262cB3.6
    • Current research topics in Advanced Algorithms 0262cB3.7
    • Selected topics in Advanced Algorithms 0262cB3.8
    • Special aspects of Advanced Algorithms 0262cB3.9
    • Advanced Network Analysis 0262cD1.1
    • Applied Machine Learning in Bioinformatics 0262cD1.12
    • Biodiversity and Evolution 0262cD1.16
    • Structural Bioinformatics 0262cD1.17
    • Applied Sequence Analysis 0262cD1.18
    • Environmental Metagenomics 0262cD1.19
    • Human Evolution 0262cD1.2
    • Current Topics in Medical Genomics 0262cD1.20
    • Computer-Aided Drug Design 0262cD1.3
    • Computational Systems Biology 0262cD1.5
    • Medical Bioinformatics 0262cD1.6
    • Machine Learning in Bioinformatics 0262cD1.7
    • Big Data Analysis in Bioinformatics 0262cD1.8
    • Complex Data Analysis in Physiology 0262cD1.9
    • Selected Topics in Bioinformatics B 0262cD2.8
    • Accompanying colloquium 0262cE1.2