Bioinformatics

• Complex Systems in Bioinformatics

  0262cB1.1
  • 19405201 Lecture
    Complex Systems in Bioinformatics (Martin Vingron, Max von Kleist, Jana Wolf)
    Schedule: Di 12:00-14:00 (Class starts on: 2025-04-15)
    Location: A3/SR 120 (Arnimallee 3-5)
    

    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 (Martin Vingron, Max von Kleist, Jana Wolf)
    Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
    Location: A3/SR 120 (Arnimallee 3-5)
    
  • 19405211 Seminar
    Seminar for Complex Systems in Bioinformatics (Martin Vingron, Max von Kleist, Jana Wolf)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: A3/SR 119 (Arnimallee 3-5)
    

• Ethics and Policy Questions

  0262cB1.13
  • 60103407 Integrierte Veranstaltung
    Ethics and Policy Questions (Ulrike Grittner, Fabian Prasser, Daniel Strech)
    Schedule: Di 16:00-18:00 (Class starts on: 2025-04-15)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

    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 (Knut Reinert)
    Schedule: -
    Location: keine Angabe
    

    Comments

    Please contact your advisor.

    Further information on our homepage.

• 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 (Class starts on: 2025-04-14)
    Location: T9/SR 006 Seminarraum (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: 2025-04-16)
    Location: A6/SR 031 Seminarraum (Arnimallee 6)
    

• Advanced Algorithms for Bioinformatics

  0262cB3.1
  • 19405301 Lecture
    Advanced Algorithms in Bioinformatics (Hugues Richard)
    Schedule: Di 10:00-12:00 (Class starts on: 2025-04-15)
    Location: T9/046 Seminarraum (Takustr. 9)
    

    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
    Practice seminar for Advanced Algorithms in Bioinformatics (Jonas Schulte-Mattler)
    Schedule: Di 08:00-10:00 (Class starts on: 2025-04-15)
    Location: T9/046 Seminarraum (Takustr. 9)
    
  • 19405311 Seminar
    Seminar for Advanced Algorithms in Bioinformatics (Hugues Richard)
    Schedule: Do 12:00-14:00 (Class starts on: 2025-04-17)
    Location: T9/046 Seminarraum (Takustr. 9)
    

• Methodology for Clinical Trials

  0262cD1.10
  • 60102501 Lecture
    Resampling techniques and their application (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 032 Seminarraum (Arnimallee 6)
    

    Comments

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

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

• Advanced Biometrical Methods

  0262cD1.11
  • 60102501 Lecture
    Resampling techniques and their application (Frank Konietschke)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 032 Seminarraum (Arnimallee 6)
    

    Comments

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

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

• Applied Machine Learning in Bioinformatics

  0262cD1.12
  • 19403613 Lab Seminar
    Machine Learning and AI in the Life Sciences: Methods and Applications (Tim Conrad, Christoph Tycowicz)
    Schedule: Fr 08:00-12:00 (Class starts on: 2025-04-25)
    Location: T9/049 Seminarraum (Takustr. 9)
    

    Additional information / Pre-requisites

    Prerequisites:

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

    Comments

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

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

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

  • 60103513 Lab Seminar
    Computer vision for biomedical images (Sören Lukassen)
    Schedule: Do 14:00-18:00 (Class starts on: 2025-04-17)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Comments

    Imaging techniques have become an integral component of both biomedical research and clinical practice. At the same time, automated image analysis has experienced significant progress, driving technologies ranging from image search to autonomous vehicles. This automation is increasingly applied to biomedical images. However, adapting computer vision algorithms for biomedical images introduces unique challenges due to their distinct properties uncommon in other imaging datasets.

    In this course, you will explore the prevalent imaging modalities within biomedicine, including CT scans, MRI, ultrasound, and whole-slide microscopy images. We will investigate the common data formats for these images, understanding what distinguishes them from typical jpeg or png files, and how these distinctions can facilitate their efficient analysis. After an introduction to basic computer vision algorithms, we will trace the evolution of classification and segmentation models in this domain over the last decade, starting with convolutional neural networks and culminating with cutting-edge architectures such as vision transformers. Through practical exercises, you will apply your knowledge to a dataset of histology slide images from cancer patients, aiming to predict the tumor's stage and location. Additionally, we will investigate how your models arrive at their predictions, identifying the data patterns they consider informative and connecting these insights to the pathophysiological alterations within tumor tissues.

    Our models will be built using the pytorch package in Python. While familiarity with coding neural networks is not a prerequisite, prior experience in general Python programming is expected.

    By the conclusion of this course, you will be equipped to develop segmentation and classification models for biomedical images, recognize common pitfalls and artifacts, explain how your models arrive at their predictions, and effectively communicate your findings.

• Applied Sequence Analysis

  0262cD1.18
  • 19400313 Lab Seminar
    Applied Sequence Analysis (Sandro Andreotti)
    Schedule: Fr 12:00-16:00 (Class starts on: 2025-04-25)
    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.

    Course structure:

    After an introduction to a workflow management system, students will independently develop increasingly complex analysis pipelines for typical bioinformatics problems such as

    - genome assembly, annotation, comparisons, phylogeny,...

    - differential gene expression analysis 

    - metagenomic analysis 

    All steps, from quality control and pre-processing to statistical analysis, will be considered and implemented using existing software. It is also ensured that the implementations are reproducible, portable and scalable.

    By the end of the course, you will be able to develop solutions to complex bioinformatics problems, think about possible pitfalls and react to them. You will also have a good overview of existing programs for different applications and know how to use/adapt some tools for other purposes. In addition, you will have mastered the standard file formats associated with sequence data, genomes, annotations, variant calling, etc., and will have developed a feel for working with (realistically) large data sets.

     

• Human Evolution

  0262cD1.2
  • 23784a Lecture
    V Human Evolution (Vladimir Jovanovic, Katja Nowick, Vanessa Schulmann)
    Schedule: 1. Block: täglich 14.04. - 05.05.2025; 10:00 - 12:00 (Class starts on: 2025-04-14)
    Location: Seminarraum 010/011 (Königin-Luise-Straße 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, Vanessa Schulmann)
    Schedule: 1. Block: täglich 14.04. - 05.05.2025; 12:00 - 13:00 (Class starts on: 2025-04-14)
    Location: Seminarraum 010/011 (Königin-Luise-Str. 1/3)
    

    Information for students

    Additional module information: Modulbeschreibung der Modulvariante Human Evolution

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

    Additional information / Pre-requisites

    Please bring your laptop to the course!

    Comments

    Further discussions of topics of the lectures

  • 23784c PC-based Seminar
    S II Human Evolution (Vladimir Jovanovic, Katja Nowick, Vanessa Schulmann)
    Schedule: 1. Block: täglich 14.04. - 05.05.2025; 13:00 - 17:00 (Class starts on: 2025-04-14)
    Location: Seminarraum 010/011 (Königin-Luise-Str. 1/3)
    

    Information for students

    Additional module information: Modulbeschreibung der Modulvariante Human Evolution

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

    Additional information / Pre-requisites

    Please bring your laptop to the course!

    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, Martin Hölzer)
    Schedule: Zweiwöchige Blockveranstaltung in den Semesterferien (Class starts on: 2025-09-29)
    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
    Current topics in cell physiology (Dorothee Günzel)
    Schedule: zweiwöchige Blockveranstaltung
    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)

     

  • 60103513 Lab Seminar
    Computer vision for biomedical images (Sören Lukassen)
    Schedule: Do 14:00-18:00 (Class starts on: 2025-04-17)
    Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)
    

    Comments

    Imaging techniques have become an integral component of both biomedical research and clinical practice. At the same time, automated image analysis has experienced significant progress, driving technologies ranging from image search to autonomous vehicles. This automation is increasingly applied to biomedical images. However, adapting computer vision algorithms for biomedical images introduces unique challenges due to their distinct properties uncommon in other imaging datasets.

    In this course, you will explore the prevalent imaging modalities within biomedicine, including CT scans, MRI, ultrasound, and whole-slide microscopy images. We will investigate the common data formats for these images, understanding what distinguishes them from typical jpeg or png files, and how these distinctions can facilitate their efficient analysis. After an introduction to basic computer vision algorithms, we will trace the evolution of classification and segmentation models in this domain over the last decade, starting with convolutional neural networks and culminating with cutting-edge architectures such as vision transformers. Through practical exercises, you will apply your knowledge to a dataset of histology slide images from cancer patients, aiming to predict the tumor's stage and location. Additionally, we will investigate how your models arrive at their predictions, identifying the data patterns they consider informative and connecting these insights to the pathophysiological alterations within tumor tissues.

    Our models will be built using the pytorch package in Python. While familiarity with coding neural networks is not a prerequisite, prior experience in general Python programming is expected.

    By the conclusion of this course, you will be equipped to develop segmentation and classification models for biomedical images, recognize common pitfalls and artifacts, explain how your models arrive at their predictions, and effectively communicate your findings.

• Computational Systems Biology

  0262cD1.5
  • 19400813 Lab Seminar
    Computational Systems Biology (Jana Wolf)
    Schedule: Termine siehe LV-Details (Class starts on: 2025-09-08)
    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 (Class starts on: 2025-04-14)
    Location: A6/SR 025/026 Seminarraum (Arnimallee 6)
    

    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 (Philipp Florian Benner, Hugues Richard)
    Schedule: Mi 08:00-10:00 (Class starts on: 2025-04-16)
    Location: A7/SR 031 (Arnimallee 7)
    

• Complex Data Analysis in Physiology

  0262cD1.9
  • 60102701 Lecture
    Complex Data Analysis in Physiology (Dorothee Günzel)
    Schedule: Mo 14:30-18:30 (Class starts on: 2025-04-14)
    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: s. Vorlesung
    Location: keine Angabe
    

• 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: 2025-04-14)
    Location: T9/053 Seminarraum (Takustr. 9)
    

    Comments

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

    Suggested reading

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

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

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

    Comments

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

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

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

    Comments

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

    Examples of approaches covered include:

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

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

  • 19404811 Seminar
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/051 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.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Time: Wedneysday 10:00am First occasion: 23.April Last occasion: 23.July
    Location: Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77
    

    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

    Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77 Time: Wedneysday 10:00am   First occasion: 23.April Last occasion: 23.July

  • 19406611 Seminar
    Journal Club: Biomedical Data Science (Katharina Jahn)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    

    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.

  • 60103311 Seminar
    Spatial Sequencing Analysis (Journal Club) (Christian Conrad)
    Schedule: Fr 10:00-12:00 (Class starts on: 2025-04-25)
    Location: A6/SR 009 Seminarraum (Arnimallee 6)
    

    Comments

    Abstrakt:  In principle, NGS sequencing is also an imaging technology. Latest methods allow to sequence DNA, RNA and proteins in tissues/cells directly and in combination. Together we will identify recent publications, especially how to process and analyze these spatial sequencing data sets with machine learning. All participants have to give 1-2 talks themselves. Please find more details: https://iimaging.org/openings/ For question or more information please contact: christian.conrad@bih-charite.de Dates by arrangement.

• 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: 2025-04-14)
    Location: T9/053 Seminarraum (Takustr. 9)
    

    Comments

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

    Suggested reading

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

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

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

    Comments

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

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

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

    Comments

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

    Examples of approaches covered include:

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

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

  • 19404811 Seminar
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/051 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.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Time: Wedneysday 10:00am First occasion: 23.April Last occasion: 23.July
    Location: Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77
    

    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

    Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77 Time: Wedneysday 10:00am   First occasion: 23.April Last occasion: 23.July

  • 19406611 Seminar
    Journal Club: Biomedical Data Science (Katharina Jahn)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    

    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.

  • 60103311 Seminar
    Spatial Sequencing Analysis (Journal Club) (Christian Conrad)
    Schedule: Fr 10:00-12:00 (Class starts on: 2025-04-25)
    Location: A6/SR 009 Seminarraum (Arnimallee 6)
    

    Comments

    Abstrakt:  In principle, NGS sequencing is also an imaging technology. Latest methods allow to sequence DNA, RNA and proteins in tissues/cells directly and in combination. Together we will identify recent publications, especially how to process and analyze these spatial sequencing data sets with machine learning. All participants have to give 1-2 talks themselves. Please find more details: https://iimaging.org/openings/ For question or more information please contact: christian.conrad@bih-charite.de Dates by arrangement.

• 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: 2025-04-14)
    Location: T9/053 Seminarraum (Takustr. 9)
    

    Comments

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

    Suggested reading

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

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

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

    Comments

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

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

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

    Comments

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

    Examples of approaches covered include:

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

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

  • 19404811 Seminar
    Computational Meta-Omics (Thilo Muth)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/051 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.

  • 19406411 Seminar
    Journal Club: Public Health Data Science (Max von Kleist)
    Schedule: Time: Wedneysday 10:00am First occasion: 23.April Last occasion: 23.July
    Location: Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77
    

    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

    Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77 Time: Wedneysday 10:00am   First occasion: 23.April Last occasion: 23.July

  • 19406611 Seminar
    Journal Club: Biomedical Data Science (Katharina Jahn)
    Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
    Location: T9/SR 006 Seminarraum (Takustr. 9)
    

    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.

  • 60103311 Seminar
    Spatial Sequencing Analysis (Journal Club) (Christian Conrad)
    Schedule: Fr 10:00-12:00 (Class starts on: 2025-04-25)
    Location: A6/SR 009 Seminarraum (Arnimallee 6)
    

    Comments

    Abstrakt:  In principle, NGS sequencing is also an imaging technology. Latest methods allow to sequence DNA, RNA and proteins in tissues/cells directly and in combination. Together we will identify recent publications, especially how to process and analyze these spatial sequencing data sets with machine learning. All participants have to give 1-2 talks themselves. Please find more details: https://iimaging.org/openings/ For question or more information please contact: christian.conrad@bih-charite.de Dates by arrangement.

• Special Aspects of Bioinformatics A

  0262cD2.4
  • 19336901 Lecture
    Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

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

  • 19336902 Practice seminar
    Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

• Special Aspects of Bioinformatics B

  0262cD2.5
  • 19336901 Lecture
    Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

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

  • 19336902 Practice seminar
    Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

• Special Aspects of Bioinformatics C

  0262cD2.6
  • 19336901 Lecture
    Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

    Comments

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

  • 19336902 Practice seminar
    Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
    Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
    Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    

• Modules w/o courses in this semester

  51 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
  • 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