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  • 4th Disease Maps Community Meeting - DMCM2019

    2 - 4 October 2019

    Sevilla, Spain

    Elixir node event
    4th Disease Maps Community Meeting - DMCM2019 The [4th Disease Maps Community Meeting] ( is hosted by the [Clinical Bioinformatics Area] (, FPS, Hospital Virgen del Rocio. **Invited Talks** * _Schizophrenia Map: Data to knowledge to data_ **Jessica Dale Tenenbaum**, Duke University at Durham, North Carolina, USA * _Logic modeling to integrate disease maps and various omics data_ **Julio Saez-Rodriguez**, RWTH-Aachen University Hospital, Aachen, Germany * _Toward whole-cell computational models for precision medicine_ **Jonathan Karr**, Icahn School of Medicine at Mount Sinai, New York, USA * _Computational approaches to tackle chemoresistance in high-grade serous ovarian cancer_ **Sampsa Hautaniemi**, Faculty of Medicine, University of Helsinki, Finland **Afternoon discussion sessions** Afternoon breakout discussion sessions are planned following the example of the 2nd Disease Maps Community Meeting in Luxembourg. We invite proposals: a title and a brief description of the topic (1 page maximum). 4-6 topics will be selected, introduced 2nd October and discussed 3rd and 4th October with summaries presented at the end to all the participants. **Abstract subimission and registration** 2019-10-02 09:30:00 UTC 2019-10-04 13:30:00 UTC Disease Maps Project Centro de Documentación Clínica Avanzada, s/n, Avda de Manuel Siurot, Sevilla, Spain Centro de Documentación Clínica Avanzada, s/n, Avda de Manuel Siurot Sevilla Spain 41013 Systems medicine Translational medicine Bioinformatics Fundación Progreso y Salud (FPS)Hospital Virgen del Rocio [] [] meetings_and_conferences [] BioinformaticsClinical BioinformaticsDiseasesDisease mapsTranslational Bioinformaticssystems medicine
  • Joint SIB / NBIS Autumn School Single Cell Analysis

    13 - 19 October 2019


    Elixir node event
    Joint SIB / NBIS Autumn School Single Cell Analysis #training #url: contact: This course is organised by NBIS/SciLifeLab and the SIB PhD Training Network. Priority is given to their members, but is open to everyone. Overview Single-cell analysis of the various -omics makes possible to discover mechanisms, strains, expressions that would not be noticed when studying bulk cell populations. New developments lead more and more research groups to use this technology and this Autumn School will provide an overview of the various methods and fields of application through lectures and hands-on exercises. Generally, the afternoons will be dedicated to practical exercises where you will be able to apply the theoretical concepts learned during the morning session. Sometimes there will be a mix of both. One afternoon will be devoted to a social activity. See the preliminary program below for more details... Audience This course is addressed to PhD students, postdocs and scientists starting to work with single cell technologies and analysis. Learning objectives The first objective of this school is to provide participants with a broad knowledge of single cell analysis that would enable them to understand its application in general. This would be achieved via the multiple lectures provided by our lecturers throughout the week. The second objective is to enable participants to apply single cell analysis in their own research. This would be achieved by all the exercises that would follow the theoretical lectures. The third objective is networking with the lecturers and also the other participants that will most likely share similar interests. Prerequisites Knowledge / competencies Students should have intermediate to advanced R skills (able to read, understand and write R scripts), know how to use common R/Bioconductor packages, be familiar with the typical steps involved in a bulk RNA-seq (both experimental and data analysis parts). Technical Students should be able to bring their own laptop with R and RStudio installed. A list of required R-packages will be sent before the course and these should be installed prior to the course start. Program Sunday ≈17-18h: arrival of the participants, check-in Welcome and dinner Monday: Transcriptomics Introductory lecture: Vincent Gardeux, Laboratory of Systems and Genetics, EPFL / SIB, Lausanne, Switzerland. Quantification, QC & Normalization: Davide Risso, Department of Statistical Sciences, University of Padova, Italy. Dimensionality reduction: Paulo Czarnewski, NBIS, Uppsala University, SciLifeLab, Uppsala, Sweden. Tuesday: Transcriptomics Batch correction: Panagiotis Papasaikas, Computational Biology Group, Friedrich Miescher Institute for Biomedical Research / SIB, Basel. Switzerland. Clustering - methods overview: Charlotte Soneson, Computational Biology Group, Friedrich Miescher Institute for Biomedical Research / SIB, Basel, Switzerland. Cell fate mapping and trajectories: Robrecht Cannoodt, VIB-UGent Center for Inflammation Research, Gent, Belgium. Wednesday: Transcriptomics Differential expression: Charlotte Soneson, Computational Biology, Friedrich Miescher Institute for Biomedical Research / SIB, Basel, Switzerland. Afternoon: Team activity Evening: Keynote lecture: Alejandro Sifrim, Laboratory of Reproductive Genomics, KU Leuwen, Belgium. Thursday: Proteomics Transcriptome + proteome: Johan Reimegård, Uppsala University, SciLifeLab, Uppsala, Sweden. Differential abundance and differential state analysis of single cell cytometry data: Mark Robinson & Helena Crowell, Statistical Genomics, University of Zurich / SIB, Zurich, Switzerland. Friday: Other omics and integration Spatial mapping of scRNAseq: Lars Borm, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. Lineage tracing and scRNA: Maria Florescu, Hubrecht Institute, Developmental Biology and Stem Cell Research, Utrecht, Netherlands. Data integration methods: Sebastien Smallwood, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. End of the event around 14h30. Application Before applying, be sure your profile matches the prerequisites and do not forget to take this survey. If you do not take the survey, your application will not be taken into consideration. Please use the same email address for the application and the survey. Registration fees are the following: 250 Swiss Francs for members of the SIB PhD Training Network and for PhD students enrolled at a Swedish University, 600 Swiss Francs for other academics, 1200 Swiss Francs for for-profit institutions. This includes full course fees, full board accommodation at the hotel and coffee breaks. Deadline for application and free-of-charge cancellation is set is set to 16 September 2019. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions. You will be informed by email of your registration confirmation. Click to apply Venue and Time Location: Hotel Central Residence & Spa, Leysin, Switzerland. Arrival: Sunday before 18h Departure: Friday around 14h30 Additional information Coordination: Björn Nystedt (NBIS/SciLifeLab), Grégoire Rossier (SIB). Scientific Committe: Åsa Björklund (NBIS/SciLifeLab), Michael Stadler & Charlotte Soneson (SIB and FMI Basel), Vincent Gardeux (EPFL). For more information, please contact Grégoire Rossier. 2019-10-13 09:00:00 UTC 2019-10-19 00:00:00 UTC Leysin Leysin [] [] [] [] [] []
  • Software Carpentry Workshop

    16 - 18 October 2019

    Heidelberg, Germany

    Software Carpentry Workshop Educators: Malvika Sharan, Georg Zeller, Mike Smith, Thomas Schwarzl, Frank Thommen (HD-HuB), Holger Dinkel Date: 16-10-2019 - 18-10-2019 09:00-18:00 Location: ATC Computer Training Lab, EMBL Heidelberg Contents: Computation is an integral part of today's research as data has grown too large or too complex to be analysed by hand. An ever-growing fraction of science is performed computationally and many wet-lab biologists spend part of their time on the computer. Many scientists struggle with this aspect of research as they have not been properly trained in the necessary set of skills. The result is that too much time is spent using inefficient tools when progress could be faster. This course provides training in several key tools, with a focus on good development practices that encourage efficient and reproducible research computing. Topics covered include: Introduction to Python scripting Introduction to the Unix shell and usage of cluster resources Version control with Git and Github Analysis pipeline management Scientific Python & working with biological data Literate programming with Jupyter notebooks Learning goals: This course aims to teach software writing skills and best practices to researchers in biology who wish to analyse data, and to introduce a toolset that can help them in their work. The goal is to enable them to be more productive and to make their science better and more reproducible. Prerequisites: This is a course for researchers in the life sciences who are using computers for their analyses, even if not full time. The target student will be familiar with some command line/programmatic computer usage, will want to become more confident using these tools efficiently and reproducibly. A target student will have written a for loop in some language before, but will not know what git is (or at least not be very comfortable using git). Keywords: Programming; Command Line; Version Control; Bioinformatics; Data Analysis; Cluster Computing Tools: Python; Bash; Unix/Linux; Git; GitHub; SnakeMake; Biopython; Pandas; Numpy; SciPy; Matplotlib 2019-10-16 09:00:00 UTC 2019-10-18 17:00:00 UTC de.NBI / ELIXIR Heidelberg, Heidelberg, Germany Heidelberg Heidelberg Karlsruhe Germany [] [] [] workshops_and_courses [] []
  • Machine Learning in R

    6 - 7 November 2019

    Heidelberg, Germany

    Machine Learning in R Date Nov 6 - Nov 7 2019 Location EMBL Heidelberg Tutors and helpers - Dr. Malvika Sharan - Prof Bernd Bischl - Martin Binder - Giuseppe Casalicchio Affiliation: Ludwig-Maximilians-University Munich Course Information This two-day course, on the implementation of Machine Learning in R, using mlr package will be delivered as practical sessions on programming and data analysis. The main goal of mlr is to provide a unified interface for machine learning tasks as classification, regression, cluster analysis and survival analysis in R. Sessions will be driven by many practical exercises and case studies. Before this workshop, participants are expected to review the official material introducing the principle of Machine Learning (see the prerequisite). Course Content This 2-day course will cover hands-on sessions using `mlr` and other relevant packages. Daily schedule - 09:30-12:30 3h morning, 90 min Theory + 90 min Practical - 12:30-13:30 1h Lunchbreak - 13:30-16:30 3h afternoon, 90 min Theory + 90 min Practical - 16:30-17:00 Time for general questions Day 1 Introduction to the concepts and Practical with mlr - Performance Evaluation and Resampling (Metrics, CV, ROC) - Introduction to Boosting Day 2 Introduction to the concepts and Practical with mlr - Tuning and Nested Cross-Validation - Regularization and Feature Selection Prerequisite The course is aimed at advanced R programmers, preferably with some knowledge of statistics and data modeling (See prerequisite materials from Day-1, 2, & 4). In this course, our learners will learn more about machine learning and its application and implementation through the hands-on sessions and use cases. Optional: Discussion-Based Session On The Principle of Machine Learning Anna Kreshuk (EMBL Group Leader) will lead a one-day discussion-based session on 14 October 2019 to address your questions on the prerequisite materials on the principle of Machine Learning. This will also allow you to connect with other participants of this workshop informally, and discuss the materials in smaller groups. Please register for this workshop separately: Registration Please register on this page: Please note that the maximum capacity of this course is 40 participants and registration is required to secure a place. If you have any questions, please contact Malvika Sharan. In your registration, please mention your EMBL group name, or institute's name (e.g. DKFZ, Uni-HD) if you are registering as an external participant. Costs 60,00 EUR Keywords: Machine Learning, R 2019-11-06 09:00:00 UTC 2019-11-07 17:00:00 UTC de.NBI Heidelberg, Heidelberg, Germany Heidelberg Heidelberg Karlsruhe Germany [] [] [] workshops_and_courses [] []
  • NBIS course (prel.): Reproducible Research

    19 - 22 November 2019

    Göteborg, Sweden

    Elixir node event
    NBIS course (prel.): Reproducible Research More information will follow: questions? mail 2019-11-19 09:00:00 UTC 2019-11-22 00:00:00 UTC Göteborg, Sweden Göteborg Sweden [] [] [] [] [] []
  • CABANA Workshop: Analysis of Crop Genomics Data

    2 - 6 December 2019

    Bogota, Colombia

    Elixir node event
    CABANA Workshop: Analysis of Crop Genomics Data This course will introduce crop biologists to methods and approaches for analysing crop genomics data. 2019-12-02 08:30:00 UTC 2019-12-06 12:45:00 UTC Universidad de los Andes - UniAndes, Bogota, Colombia Universidad de los Andes - UniAndes Bogota Colombia 111711 [] Marco Cristancho [] [] [] [] HDRUK
  • A tour of machine learning: classification

    13 - 14 January 2020

    Gent, Belgium

    Elixir node event
    A tour of machine learning: classification Machine learning has become ubiquitous in biotechnology (as in many other fields), fueled largely by the increasing availability and amount of data. Learning algorithms can figure out how to perform important tasks by generalizing examples. Typical applications are diagnoses/prognoses, gene/protein annotation, drug design, image recognition, text mining and many others. However, building successful machine learning models requires a substantial amount of “black art” that is hard to find in textbooks. This course is an interactive Jupyter Notebook (Python) that will teach you how to build successful machine learning models. No background in machine learning is assumed, just a keen interest. 2020-01-13 09:00:00 UTC 2020-01-14 00:00:00 UTC VIB Bioinformatics Core iGent, Gent, Belgium iGent Gent Belgium 9052 [] [] [] [] [] []
  • Mass spectrometry data processing

    2 - 3 June 2020

    Gent, Belgium

    Elixir node event
    Mass spectrometry data processing Obtain a good understanding of the origins and properties of mass spec data Obtain an understanding of the processing of mass spec data, aimed at identifying and quantifying peptides and proteins Gain sufficient understanding of the software tools and database used, and of the issues and caveats involved, to critically analyse and assess results from mass spectrometry based proteomics experiments 2020-06-02 09:00:00 UTC 2020-06-03 00:00:00 UTC VIB Bioinformatics Core iGent, Gent, Belgium iGent Gent Belgium 9052 [] [] [] [] [] []
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