SoSe 23: BUA Joint Degree Programs

• Complex Systems in Bioinformatics

  0262cB1.1
  • 19405201 Lecture
    Complex Systems in Bioinformatics (Alexander Bockmayr, Max von Kleist, Martin Vingron)
    Schedule: Di 12:00-14:00 (Class starts on: 2023-04-18)
    Location: A6/SR 025/026 Seminarraum (Arnimallee 6)
    

    Comments

    Students have acquired a deeper understanding of fundamental mathematical and algorithmic concepts in the field of modeling, simulation and analysis of complex biological systems against the background of current research trends in system biology and biotechnology. They are capable of analyzing a given biological or medical problem, selecting a suitable modeling approach, independently developing a solution and assessing and communicating the results.

    Content:

    Topics from the following areas are considered in depth:

    - Network structure analysis

    - Graphical modeling

    - Modeling of biochemical networks using standard differential equations

    - Discrete modeling of regulatory networks

    - Constraint-based modeling

    - Stochastic and hybrid modeling

    Suggested reading

    wird in der Veranstaltung bekanntgegeben.

  • 19405202 Practice seminar
    Practice seminar for Complex Systems in Bioinformatics (Alexander Bockmayr, Max von Kleist, Martin Vingron)
    Schedule: Fr 10:00-12:00 (Class starts on: 2023-04-21)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    
  • 19405211 Seminar
    Seminar for Complex Systems in Bioinformatics (Alexander Bockmayr, Max von Kleist, Martin Vingron)
    Schedule: Fr 12:00-14:00 (Class starts on: 2023-04-21)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Ethics and Policy Questions

  0262cB1.13
  • 60103407 Integrierte Veranstaltung
    Ethics an Policy Questions (Ulrike Grittner, Fabian Prasser, Daniel Strech)
    Schedule: Do 16:00-18:00 (Class starts on: 2023-04-20)
    Location: T9/SR 005 Übungsraum (Takustr. 9)
    

    Comments

    Basic scientific and philosophical concepts are conveyed for dealing with bioethical issues. Topics are dealt with such as big data and health, fertilization, embryo adoption, three-parent babies, reproductive and therapeutic cloning, genetic diagnosis, alterations to plant, animal and human genomes, human-animal beasts, brain death and organ donation, vaccination as a duty. The participants learn to make well-founded judgments on relevant bioethical issues.

• Research Practical

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

    Comments

    Please contact your advisor.

    Further information on our homepage.

• Data Science in the Life Sciences

  0590aB2.1
  • 19405606 Seminar-style instruction
    Data Science in the Life Sciences (Katharina Jahn)
    Schedule: Mo 10:00-14:00, zusätzliche Termine siehe LV-Details (Class starts on: 2023-04-17)
    Location: T9/SR 005 Übungsraum (Takustr. 9)
    

    Comments

    This course offers an introduction to various types of data and analysis techniques which are typically used in the life sciences (e.g. omics technologies). The goal is to get a deeper understanding of advanced concepts and data analytical methods in the area of life sciences.

    The focus will be on the following topics:

    * acquisition and pre-processing of data from the area of life sciences,
    * explorative analysis techniques,
    * concepts and tools for reproducible research,
    * theory and practice of methods and models for the analysis of data from the life sciences (statistical inference, regression models, methods of machine learning),
    * introduction to methods of big data analysis.

    After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.

     

  • 19405612 Project Seminar
    Projectseminar for Data Science in the Life Sciences (Katharina Jahn)
    Schedule: Mi 10:00-14:00 (Class starts on: 2023-04-19)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    

• Data Science in the Life Sciences

  0590bB1.1
  • 19405606 Seminar-style instruction
    Data Science in the Life Sciences (Katharina Jahn)
    Schedule: Mo 10:00-14:00, zusätzliche Termine siehe LV-Details (Class starts on: 2023-04-17)
    Location: T9/SR 005 Übungsraum (Takustr. 9)
    

    Comments

    This course offers an introduction to various types of data and analysis techniques which are typically used in the life sciences (e.g. omics technologies). The goal is to get a deeper understanding of advanced concepts and data analytical methods in the area of life sciences.

    The focus will be on the following topics:

    * acquisition and pre-processing of data from the area of life sciences,
    * explorative analysis techniques,
    * concepts and tools for reproducible research,
    * theory and practice of methods and models for the analysis of data from the life sciences (statistical inference, regression models, methods of machine learning),
    * introduction to methods of big data analysis.

    After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.

     

  • 19405612 Project Seminar
    Projectseminar for Data Science in the Life Sciences (Katharina Jahn)
    Schedule: Mi 10:00-14:00 (Class starts on: 2023-04-19)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    

• Advanced Algorithms for Bioinformatics

  0262cB3.1
  • 19405301 Lecture
    Advanced Algorithms in Bioinformatics (Knut Reinert)
    Schedule: Di 10:00-12:00 (Class starts on: 2023-04-18)
    Location: A3/SR 119 (Arnimallee 3-5)
    

    Comments

    Goals:

    The students will gain a deeper unterstanding for basic algorithmic concepts for the analysis of genomic sequencing related to state of the art research in bioinformatics and biotechnology. They will learn various paradigms for the approximate search. They will know which algorithms should be preferred under what circumstances and are able to grasp key concepts of scientific publications related to this field.

    Some examples of subjects that will be more deeply discussed:

    • Paradigms for approximative, semiglobal alignments (read mapping)
    • Methods for genomic assembly and metagenomic assembly
    • Methods for the identification of genetic variants (SNVs, SNPs, CNVs) - algorithmic problems of quantifying expession using NGS data

    For further information go to: https://mycampus.imp.fu-berlin.de/portal

  • 19405302 Practice seminar
    Übung zu Advanced Algorithms in Bioinformatics (Knut Reinert)
    Schedule: Di 08:00-10:00 (Class starts on: 2023-04-18)
    Location: A3/SR 119 (Arnimallee 3-5)
    
  • 19405311 Seminar
    Seminar zu Advanced Algorithms in Bioinformatics (Knut Reinert)
    Schedule: Do 14:00-16:00 (Class starts on: 2023-04-20)
    Location: T9/055 Seminarraum (Takustr. 9)
    

• Advanced Biometrical Methods

  0262cD1.11
  • 60102301 Lecture
    Statistical Methods for Small Sample Sizes (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    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: 2023-04-19)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Applied Machine Learning in Bioinformatics

  0262cD1.12
  • 19403613 Lab Seminar
    Applied Machine Learning in Bioinformatics (Tim Conrad)
    Schedule: Fr 08:00-12:00 (Class starts on: 2023-04-21)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Additional information / Pre-requisites

    Prerequisites:

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

    Comments

    In this course, you will learn the basic statistical and algorithmic concepts in Machine Learning with a focus on their practical applications in the field of bioinformatics. You will have the opportunity to work on practical problems and implement and use the methods learned during lectures to analyze biological datasets, in particular omics data. For this, you should have experience in programming languages, such as R, Python, Java or C/C++.
    
    We will cover pre-processing of biological data, model implementations and analysis methods. You will learn about models for regression, clustering and classification, feature selection and advanced data preprocessing, such as imputation. We will also cover Deep Learning approaches.
    
    Throughout the course, you will complete weekly exercises and present your results to the class. These exercises are designed to reinforce the practical applications of the material covered in lectures.
    
    By the end of the course, you will be able to process data, choose appropriate models to answer specific questions, evaluate results, and effectively communicate your findings through written reports.

• Applied Sequence Analysis

  0262cD1.18
  • 19400313 Lab Seminar
    Applied Sequence Analysis (Sandro Andreotti)
    Schedule: Fr 12:00-16:00 (Class starts on: 2023-04-21)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Comments

    Goals:

    Students will be able to solve a variety of bioinformatics analysis tasks. They have a broad knowledge on available software and are able to combine them in complex analysis workflows - using a workflow management system - with a strong focus on reproducibility and portability.

• Human Evolution

  0262cD1.2
  • 23784a Lecture
    V Human Evolution (Vladimir Jovanovic, Katja Nowick, Vladimir Bajic)
    Schedule: Block: 22.05. - 12.06.2023; 10:00 - 12:00 (Class starts on: 2023-05-22)
    Location: Seminarraum I (R 1) (Königin-Luise-Str. 1 / 3)
    

    Information for students

    Course as part of the modul 'Computational Biology'

    Additional information / Pre-requisites

    Please bring your laptop to the course!

    Comments

    The focus will be on molecular human evolution and include topics such as:
    Comparison of humans to other primates at the level of genomes, transcriptomes, phenotypes, cognitive abilities Archaic humans, Neolithic revolution, Modern humans, Adaptation, Evolutionary medicine

  • 23784b Seminar
    S I Human Evolution (Vladimir Jovanovic, Katja Nowick, Vladimir Bajic)
    Schedule: 2. Block: 22.05. - 12.06.2023; 12:00 - 13:00 (Class starts on: 2023-05-22)
    Location: Seminarraum I (R 1) (Königin-Luise-Str. 1 / 3)
    

    Information for students

    zusätzlich 10 Plätze für Bioinformatiker; Zusätzliche Modulinformationen: Modulbeschreibung der Modulvariante Human Evolution

    UN Sustainable Development Goals (SDGs): 1, 4, 6

    Comments

    Further discussions of topics of the lectures

  • 23784c PC-based Seminar
    S II Human Evolution (Vladimir Jovanovic, Katja Nowick, Vladimir Bajic)
    Schedule: 2. Block: 22.05. - 12.06.2023; 13:00 - 15:00 (Class starts on: 2023-05-22)
    Location: Seminarraum I (R 1) (Königin-Luise-Str. 1 / 3)
    

    Information for students

    zusätzlich 10 Plätze für Bioinformatiker; Zusätzliche Modulinformationen: Modulbeschreibung der Modulvariante Human Evolution

    UN Sustainable Development Goals (SDGs): 1, 4, 6

    Comments

    Using the computer, analyses in topics such as the following will be conducted: Sequence comparisons of selected genomic regions, transcriptome analyses, statistical tests for selection, genome browser, biological databases, reconstruction of migration, population genomics

• Current Topics in Medical Genomics

  0262cD1.20
  • 19406213 Lab Seminar
    SARS-CoV-2 Bioinformatics & Data Science (Max von Kleist, Nils Gubela)
    Schedule: Mo 18.09. 09:00-17:00 (Class starts on: 2023-09-18)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Comments

    We will introduce bioinformatics approaches for the analysis, surveillance and phenotypic assessment of SARS-CoV-2 and its variants of concern (VOC). This will involve approaches for SARS-CoV-2 genome reconstruction from raw sequencing data (Illumina, ONT), lineage assignment, genomic profiling and phenotypic inference, clustering of sequences, phylogeny and genome-based incidence estimation.   The students will work hands-on with real data and conduct small projects, which will be presented on week 2. A typical day in week one will consist of lectures highlighting the biological-, public health and methodological background, hands-on work followed by short concluding summaries. Towards the end of the day, the students will work in-depth on their designated projects that will be presented in week 2.

• Current Topics in Cell Physiology

  0262cD1.4
  • 60100613 Lab Seminar
    Aktuelle zellphysiologische Fragestellungen (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)

     

• Computational Systems Biology

  0262cD1.5
  • 19400813 Lab Seminar Cancelled
    Computational Systems Biology (Heike Siebert, Jana Wolf)
    Schedule: Do 10:00-14:00 (Class starts on: 2023-04-20)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Comments

    Content:

    The course will give an introduction to the modeling of molecular networks using discrete/logical approaches as well as differential equations. The theoretical frameworks will be introduced and software tools presented. On the basis of suitable reasearch articles, the participants will conduct their own modeling project in small groups.

    Target group:

    Students of Master Bioinformatics from the 2nd semester.

• Machine Learning in Bioinformatics

  0262cD1.7
  • 19405701 Lecture
    Machine Learning in Bioinformatics (Philipp Florian Benner, Hugues Richard)
    Schedule: Mo 08:00-10:00, zusätzliche Termine siehe LV-Details (Class starts on: 2023-04-17)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    This course introduces key machine learning concepts and is accompanied by tutorials and exercises where machine learning methods are applied to actual bioinformatics problems. After a short recap of probability theory, we introduce probabilistic methods for classification and sequence analysis (Naive Bayes, Mixture Models, Hidden Markov Models). We discuss Expectation Maximization (EM) from a probabilistic perspective and use it for sequence analysis. Linear and logistic regression serve as an entry point to more complex machine learning methods, including kernel methods and neural networks. The lecture covers multiple neural network architectures (CNNs, GNN, Transformers) that are currently used in the bioinformatics community and other research domains. During the tutorials and as part of homework assignments, selected machine learning models are implemented in Python using scikit-learn and pytorch. The course should enable students to understand all common machine learning techniques and devise state of the art classification strategies that can then be applied to problems in bioinformatics and related fields.
    Contents:
    - Naive Bayes
    - Clustering and Mixture Models
    - Hidden Markov Models
    - Regression and Partial Least Squares
    - Kernel Methods
    - Neural Networks and Architectures
    - Regularization and Model Selection   Requirements:
    - Linear algebra (basic vector and matrix algebra)
    - Analysis (mathematical optimization, Lagrange)
    - Programming in Python -- including object oriented programming
    - A basic understanding or keen interest in molecular biology and bioinformatics applications

  • 19405702 Practice seminar
    Practice Seminar for Machine Learning in Bioinformatics (Alexander Karl Kister, Aakash Ashok Naik, Kristin Vogel)
    Schedule: Mi 08:00-10:00 (Class starts on: 2023-04-19)
    Location: T9/049 Seminarraum (Takustr. 9)
    

• Complex Data Analysis in Physiology

  0262cD1.9
  • 60102701 Lecture
    Complex Data Analysis in Physiology (Dorothee Günzel)
    Schedule: -
    Location: keine Angabe
    

    Comments

    Joint class taught by the Institute of Clinical Physiology and the Institute of Physiology at the Charité.

    Theoretical and practical aspects of data acquisition, real-time data processing and automated pattern recognition in biomedicine. Topics from the following areas are covered in depth:

    • Data acquisition and processing of image files in research and clinical settings (e.g. live cell imaging, super-resolution microscopy, medical imaging techniques).
    • Electrophysiological methods (e.g. impedance spectroscopy, microarrays, EEG, ECG)
    • Methods and application of automated pattern recognition (e.g. automated tumour detection, real-time analysis of biological signals in the brain-computer interface or in retina implants, prediction of individual arrhythmia risks)

    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

  • 60102702 Practice seminar
    Practice seminar for Complex Data Analysis in Physiology (Dorothee Günzel)
    Schedule: -
    Location: keine Angabe
    

• Current Research Topics in Bioinformatics A

  0262cD2.1
  • 19333211 Seminar
    Seminar: Visual Computing with Information Theory (Georges Hattab)
    Schedule: Mi 12:00-14:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would delve into how information theory can be used to improve visual computing, including the use of data compression, feature extraction, and other information-theoretic methods in visual computing and information visualization.

    Information theory tools are often forgotten in visual computing. We borrow from information theory process optimization, multiplexing, laws and measures to ensure visual representation of information. This repeating seminar will couple information visualization with information theory tools.

  • 19333611 Seminar
    Seminar Deep Learning for biomedical applications (Vitaly Belik)
    Schedule: Di 16:00-18:00 (Class starts on: 2023-04-18)
    Location: T9/049 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, Kaminski 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

  • 19334111 Seminar
    Seminar: Biological Data Visualization (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would cover how visualization techniques can be used to analyze and interpret biological data, including examples and case studies of real-world applications.

    Biology data visualization is a branch of bioinformatics concerned with the application of computer gra- phics, scientific visualization, and information visualization to different areas of the life sciences. In this wee- kly seminar, we will focus on presenting methods that address problems that arise from analyzing biological data.

  • 19402911 Seminar
    Journal Club Computational Biology (Knut Reinert)
    Schedule: Mo 14:00-16:00 (Class starts on: 2023-04-24)
    Location: T9/051 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

  • 19404511 Seminar Cancelled
    Introduction to Computational Proteomics (Chris Bielow)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

    This seminar will cover central aspects of modern bottom-up proteome analysis by mass spectrometry with a focus on bioinformatic algorithms and data analysis.

    After a brief introduction to the field of mass spectrometry-based proteomics (instrument types, data properties, overview of bioinformatics challenges) we will explore the big cornerstones, including isotope models and averagines, scoring algorithms for the identification of mass spectra, false-discovery-rate estimation using decoy peptides, absolute and relative quantification of proteins and correlation between mRNA and protein expression.

  • 19404811 Seminar Cancelled
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    The growing interest in microbial communities is due to findings that demonstrate the influence of microorganisms on human health. For example, microbiome research investigates the role of intestinal microbiota in diseases such as diabetes and morbus

    Crohn or health disorders such as food allergies and obesity. In this context, an imbalanced microbiome is associated with being the cause or the consequence of certain diseases or health disorders. In order to identify and quantify the microorganisms present in experimental samples, meta-omics analyses (e.g. metagenomics, metatranscriptomics, metaproteomics) are conducted that heavily rely on computational strategies from bioinformatics.

    The main objectives of this seminar are (1) to introduce both computational and experimental meta-omics methods for analyzing single microbial and microbiome samples with a particular focus on metagenomics and metaproteomics, (2) to provide a general overview on the most commonly employed and recently proposed bioinformatics strategies in the field, and (3) to discuss the shortcomings of current meta-omics approaches in the context of microbiome research and diagnostics of bacteria and viruses.

  • 19406111 Seminar Cancelled
    Large-scale phylogeny inference (Prabhav Kalaghatgi)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    Description:

    Advances in sequencing technology have made it possible to investigate the evolutionary dynamics of rapidly evolving pathogens such as SARS-CoV2 in unprecedented detail. Phylogeny inference is an essential step in evolutionary analysis. Thus techniques for large-scale phylogeny inference are needed if one is to study the evolution of tens of thousands of viral genomes. Students participating in this seminar will be introduced to topics such as

    (i) Tree modification operations

    (ii) Numerical optimization of hidden Markov models on trees

    (ii) Topological correspondence between minimum spanning trees and phylogenetic trees

    (iii) Evolutionary placement algorithm

    Prior requirements:

    Participation in the course "Phylogeny inference and applications", or any course dealing with combinatorial optimization is helpful but not necessary.

  • 19406311 Seminar
    Computational Cancer Research (Katharina Jahn)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: A6/SR 025/026 Seminarraum (Arnimallee 6)
    

    Comments

     Modern cancer research is increasingly driven by high-volume molecular patient data, such as multi-omics and single-cell data. This type of data provides unprecedented insights into tumour biology and disease trajectories, and can be utilized to optimise targeted cancer therapies. The analysis of such complex molecular data requires specialised computational and statistical methods that are geared towards its unique technical and medical challenges. In this course, we study original research papers and discuss the current state-of-the-art of computational cancer research and its contributions to the clinical practice.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Do 10:00-12:00 (Class starts on: 2023-04-20)
    Location: Online - Link wird den Teilnehmern mitgeteilt.
    

    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.

     

• Current Research Topics in Bioinformatics B

  0262cD2.2
  • 19333211 Seminar
    Seminar: Visual Computing with Information Theory (Georges Hattab)
    Schedule: Mi 12:00-14:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would delve into how information theory can be used to improve visual computing, including the use of data compression, feature extraction, and other information-theoretic methods in visual computing and information visualization.

    Information theory tools are often forgotten in visual computing. We borrow from information theory process optimization, multiplexing, laws and measures to ensure visual representation of information. This repeating seminar will couple information visualization with information theory tools.

  • 19333611 Seminar
    Seminar Deep Learning for biomedical applications (Vitaly Belik)
    Schedule: Di 16:00-18:00 (Class starts on: 2023-04-18)
    Location: T9/049 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, Kaminski 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

  • 19334111 Seminar
    Seminar: Biological Data Visualization (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would cover how visualization techniques can be used to analyze and interpret biological data, including examples and case studies of real-world applications.

    Biology data visualization is a branch of bioinformatics concerned with the application of computer gra- phics, scientific visualization, and information visualization to different areas of the life sciences. In this wee- kly seminar, we will focus on presenting methods that address problems that arise from analyzing biological data.

  • 19402911 Seminar
    Journal Club Computational Biology (Knut Reinert)
    Schedule: Mo 14:00-16:00 (Class starts on: 2023-04-24)
    Location: T9/051 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

  • 19404511 Seminar Cancelled
    Introduction to Computational Proteomics (Chris Bielow)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

    This seminar will cover central aspects of modern bottom-up proteome analysis by mass spectrometry with a focus on bioinformatic algorithms and data analysis.

    After a brief introduction to the field of mass spectrometry-based proteomics (instrument types, data properties, overview of bioinformatics challenges) we will explore the big cornerstones, including isotope models and averagines, scoring algorithms for the identification of mass spectra, false-discovery-rate estimation using decoy peptides, absolute and relative quantification of proteins and correlation between mRNA and protein expression.

  • 19404811 Seminar Cancelled
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    The growing interest in microbial communities is due to findings that demonstrate the influence of microorganisms on human health. For example, microbiome research investigates the role of intestinal microbiota in diseases such as diabetes and morbus

    Crohn or health disorders such as food allergies and obesity. In this context, an imbalanced microbiome is associated with being the cause or the consequence of certain diseases or health disorders. In order to identify and quantify the microorganisms present in experimental samples, meta-omics analyses (e.g. metagenomics, metatranscriptomics, metaproteomics) are conducted that heavily rely on computational strategies from bioinformatics.

    The main objectives of this seminar are (1) to introduce both computational and experimental meta-omics methods for analyzing single microbial and microbiome samples with a particular focus on metagenomics and metaproteomics, (2) to provide a general overview on the most commonly employed and recently proposed bioinformatics strategies in the field, and (3) to discuss the shortcomings of current meta-omics approaches in the context of microbiome research and diagnostics of bacteria and viruses.

  • 19406111 Seminar Cancelled
    Large-scale phylogeny inference (Prabhav Kalaghatgi)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    Description:

    Advances in sequencing technology have made it possible to investigate the evolutionary dynamics of rapidly evolving pathogens such as SARS-CoV2 in unprecedented detail. Phylogeny inference is an essential step in evolutionary analysis. Thus techniques for large-scale phylogeny inference are needed if one is to study the evolution of tens of thousands of viral genomes. Students participating in this seminar will be introduced to topics such as

    (i) Tree modification operations

    (ii) Numerical optimization of hidden Markov models on trees

    (ii) Topological correspondence between minimum spanning trees and phylogenetic trees

    (iii) Evolutionary placement algorithm

    Prior requirements:

    Participation in the course "Phylogeny inference and applications", or any course dealing with combinatorial optimization is helpful but not necessary.

  • 19406311 Seminar
    Computational Cancer Research (Katharina Jahn)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: A6/SR 025/026 Seminarraum (Arnimallee 6)
    

    Comments

     Modern cancer research is increasingly driven by high-volume molecular patient data, such as multi-omics and single-cell data. This type of data provides unprecedented insights into tumour biology and disease trajectories, and can be utilized to optimise targeted cancer therapies. The analysis of such complex molecular data requires specialised computational and statistical methods that are geared towards its unique technical and medical challenges. In this course, we study original research papers and discuss the current state-of-the-art of computational cancer research and its contributions to the clinical practice.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Do 10:00-12:00 (Class starts on: 2023-04-20)
    Location: Online - Link wird den Teilnehmern mitgeteilt.
    

    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.

     

• Current Research Topics in Bioinformatics C

  0262cD2.3
  • 19333211 Seminar
    Seminar: Visual Computing with Information Theory (Georges Hattab)
    Schedule: Mi 12:00-14:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would delve into how information theory can be used to improve visual computing, including the use of data compression, feature extraction, and other information-theoretic methods in visual computing and information visualization.

    Information theory tools are often forgotten in visual computing. We borrow from information theory process optimization, multiplexing, laws and measures to ensure visual representation of information. This repeating seminar will couple information visualization with information theory tools.

  • 19333611 Seminar
    Seminar Deep Learning for biomedical applications (Vitaly Belik)
    Schedule: Di 16:00-18:00 (Class starts on: 2023-04-18)
    Location: T9/049 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, Kaminski 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

  • 19334111 Seminar
    Seminar: Biological Data Visualization (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    Location: T9/055 Seminarraum (Takustr. 9)
    

    Comments

    This seminar would cover how visualization techniques can be used to analyze and interpret biological data, including examples and case studies of real-world applications.

    Biology data visualization is a branch of bioinformatics concerned with the application of computer gra- phics, scientific visualization, and information visualization to different areas of the life sciences. In this wee- kly seminar, we will focus on presenting methods that address problems that arise from analyzing biological data.

  • 19402911 Seminar
    Journal Club Computational Biology (Knut Reinert)
    Schedule: Mo 14:00-16:00 (Class starts on: 2023-04-24)
    Location: T9/051 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

  • 19404511 Seminar Cancelled
    Introduction to Computational Proteomics (Chris Bielow)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

    This seminar will cover central aspects of modern bottom-up proteome analysis by mass spectrometry with a focus on bioinformatic algorithms and data analysis.

    After a brief introduction to the field of mass spectrometry-based proteomics (instrument types, data properties, overview of bioinformatics challenges) we will explore the big cornerstones, including isotope models and averagines, scoring algorithms for the identification of mass spectra, false-discovery-rate estimation using decoy peptides, absolute and relative quantification of proteins and correlation between mRNA and protein expression.

  • 19404811 Seminar Cancelled
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    The growing interest in microbial communities is due to findings that demonstrate the influence of microorganisms on human health. For example, microbiome research investigates the role of intestinal microbiota in diseases such as diabetes and morbus

    Crohn or health disorders such as food allergies and obesity. In this context, an imbalanced microbiome is associated with being the cause or the consequence of certain diseases or health disorders. In order to identify and quantify the microorganisms present in experimental samples, meta-omics analyses (e.g. metagenomics, metatranscriptomics, metaproteomics) are conducted that heavily rely on computational strategies from bioinformatics.

    The main objectives of this seminar are (1) to introduce both computational and experimental meta-omics methods for analyzing single microbial and microbiome samples with a particular focus on metagenomics and metaproteomics, (2) to provide a general overview on the most commonly employed and recently proposed bioinformatics strategies in the field, and (3) to discuss the shortcomings of current meta-omics approaches in the context of microbiome research and diagnostics of bacteria and viruses.

  • 19406111 Seminar Cancelled
    Large-scale phylogeny inference (Prabhav Kalaghatgi)
    Schedule: Di 14:00-16:00 (Class starts on: 2023-04-18)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Comments

    Description:

    Advances in sequencing technology have made it possible to investigate the evolutionary dynamics of rapidly evolving pathogens such as SARS-CoV2 in unprecedented detail. Phylogeny inference is an essential step in evolutionary analysis. Thus techniques for large-scale phylogeny inference are needed if one is to study the evolution of tens of thousands of viral genomes. Students participating in this seminar will be introduced to topics such as

    (i) Tree modification operations

    (ii) Numerical optimization of hidden Markov models on trees

    (ii) Topological correspondence between minimum spanning trees and phylogenetic trees

    (iii) Evolutionary placement algorithm

    Prior requirements:

    Participation in the course "Phylogeny inference and applications", or any course dealing with combinatorial optimization is helpful but not necessary.

  • 19406311 Seminar
    Computational Cancer Research (Katharina Jahn)
    Schedule: Do 08:00-10:00 (Class starts on: 2023-04-20)
    Location: A6/SR 025/026 Seminarraum (Arnimallee 6)
    

    Comments

     Modern cancer research is increasingly driven by high-volume molecular patient data, such as multi-omics and single-cell data. This type of data provides unprecedented insights into tumour biology and disease trajectories, and can be utilized to optimise targeted cancer therapies. The analysis of such complex molecular data requires specialised computational and statistical methods that are geared towards its unique technical and medical challenges. In this course, we study original research papers and discuss the current state-of-the-art of computational cancer research and its contributions to the clinical practice.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Do 10:00-12:00 (Class starts on: 2023-04-20)
    Location: Online - Link wird den Teilnehmern mitgeteilt.
    

    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.

     

• Special Aspects of Bioinformatics A

  0262cD2.4
  • 60102301 Lecture
    Statistical Methods for Small Sample Sizes (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    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: 2023-04-19)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Special Aspects of Bioinformatics B

  0262cD2.5
  • 60102301 Lecture
    Statistical Methods for Small Sample Sizes (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    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: 2023-04-19)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Special Aspects of Bioinformatics C

  0262cD2.6
  • 60102301 Lecture
    Statistical Methods for Small Sample Sizes (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2023-04-19)
    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: 2023-04-19)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Modules w/o courses in this semester

  52 Modules
  • Foundations of Computer Science 0262cA1.1
  • Foundations of Mathematics and Statistics 0262cA1.2
  • Foundations of Biomedicine 0262cA1.3
  • Introduction to Focus Areas 0262cA1.4
  • Computer-Aided Drug Design 0262cB1.10
  • Current topics in cell-physiology 0262cB1.11
  • Computational Systems Biology 0262cB1.12
  • Complex Systems in Biomedical Applications 0262cB1.2
  • 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
  • Advanced Topics in Data Management 0089cA1.29
  • 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
  • Advanced Algorithms 0089cA2.1
  • Applied Sequence Analysis 0262cB3.10
  • Environmental metagenomics 0262cB3.11
  • Current topics in structural bioinformatics 0262cB3.15
  • Methods in Life Sciences 0262cB3.16
  • 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
  • Methodology for Clinical Trials 0262cD1.10
  • Biodiversity and Evolution 0262cD1.16
  • Structural Bioinformatics 0262cD1.17
  • Environmental Metagenomics 0262cD1.19
  • Current Topics in Structural Bioinformatics 0262cD1.21
  • Computer-Aided Drug Design 0262cD1.3
  • Medical Bioinformatics 0262cD1.6
  • Big Data Analysis in Bioinformatics 0262cD1.8
  • Selected Topics in Bioinformatics A 0262cD2.7
  • Selected Topics in Bioinformatics B 0262cD2.8
  • Accompanying colloquium 0262cE1.2