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UID:165698-1728896400-1729270800@www.scilifelab.se
SUMMARY:Omics Integration and Systems Biology
DESCRIPTION:National course open for PhD students\, postdocs\, researchers\, and other employees in all Swedish universities\, in need of a general description of different approaches for working with multiple types of biological data. We also welcome applications from outside of Sweden and from the non-academic sector\, for more info contact us! \n\n\n\n\n\n\n\nImportant dates and information\n\n\n\nApplication opens: 2024-08-05 \n\n\n\nApplication closes: 2024-09-16 \n\n\n\nConfirmation to accepted students: 2024-09-23 \n\n\n\nCourse Leader and teachers: \n\n\n\nNikolay Oskolkov (Lund University\, course leader) \n\n\n\nRasool Saghaleyni (Chalmers University of Technology\, course leader) \n\n\n\nSergiu Netotea (Chalmers University of Technology\, course lecturer) \n\n\n\nJennifer Fransson (Uppsala University\, course lecturer) \n\n\n\nYuan Li (Lund University\, TA) \n\n\n\nNima Rafati (Uppsala University\, TA) \n\n\n\nIn case you miss information on any of the above dates\, please contact: \n\n\n\nNikolay Oskolkov\, nikolay.oskolkov@scilifelab.se \n\n\n\nRasool Saghaleyni\, rasools@chalmers.se \n\n\n\n\n\n\n\n\n\nApplication\n\n\n\n\n\nCourse website\n\n\n\n\n\nCourse fee\n\n\n\nA course fee* of 3000 SEK for academic participants and 15 000 SEK for non-academic participants will be invoiced to accepted participants. The fee includes all coffee breaks\, all lunches and 1 course dinner. \n\n\n\n*Please note that NBIS cannot invoice individuals \n\n\n\n\n\n\n\nCourse content\n\n\n\nThe aim of this workshop is to provide an integrated view of data-driven analysis of biological data through machine learning\, graph and network analysis as well as constraint-based modeling integration methods. A general description of different approaches for working with multiple layers of biological information\, i.e. Omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing their advantages and pitfalls. The techniques will be discussed in terms of their rationale and applicability. \n\n\n\nTopics covered will include: \n\n\n\n\nData pre-processing\, cleaning and feature selection prior to integration;\n\n\n\nApplication of key machine learning methods for multi-omics analysis including deep learning;\n\n\n\nMulti-omics factor analysis\, dimension reduction and clustering;\n\n\n\nSingle Cell and Spatial transcriptomics integration;\n\n\n\nBiological network inference\, community and topology analysis and visualization;\n\n\n\nCondition-specific and personalized modeling through Genome-scale Metabolic models for integration of transcriptomic\, proteomic\, metabolomic and fluxomic data;\n\n\n\nIdentification of key biological functions and pathways;\n\n\n\nIdentification of potential biomarkers and targetable genes through modeling and biological network analysis;\n\n\n\nApplication of network approaches in meta-analyses;\n\n\n\nSimilarity network fusion and matrix factorization techniques;\n\n\n\nIntegrated data visualization techniques\n\n\n\n\n\n\n\n\nLearning outcomes\n\n\n\nUpon completion of this course\, you will be able to: \n\n\n\n\nIdentify key methods for analysis and integration of omics data based on a given dataset;\n\n\n\nPerform  feature selection and dimension reduction techniques; \n\n\n\nUnderstand strengths and pitfalls of key machine learning techniques in multi-omic analysis;\n\n\n\nApply unsupervised and supervised machine learning data integration techniques;\n\n\n\nBuild biological networks based on different omics data including integrated multi-omics networks;\n\n\n\nPerform centrality and community analyses in graphs;\n\n\n\nApply network approaches in meta-analyses;\n\n\n\nApply similarity network fusion of patient data;\n\n\n\nCompare different cell-types or conditions through the application of different biological network analysis techniques;\n\n\n\nSimulate biological functions using constraint-based models and flux balance analysis;\n\n\n\nIdentify potential confounding factors and sources of bias.\n\n\n\n\n\n\n\n\nEntry requirements\n\n\n\nThe following is a list of skills required for being able to follow the course and complete the exercises: \n\n\n\n\nBasic knowledge in R or Python;\n\n\n\nBasic understanding of frequentist statistics;\n\n\n\n\nDesirable\, but not essential\, skills increasing the output of the course\, include: \n\n\n\n\nExperience with NGS and omics analysis\n\n\n\nCompleting “Introduction to bioinformatics using NGS data” and “Introduction to biostatistics and machine learning” NBIS courses\n\n\n\n\n\n\n\n\nDue to limited space the course can accommodate a maximum of 35 participants. If we receive more applications\, participants will be selected based on selection criteria\, including (but not limited to) correct entry requirements\, motivation to attend the course\, as well as gender and geographical balance.
URL:https://www.scilifelab.se/event/omics-integration-and-systems-biology/
LOCATION:Lund University\, Lund\, Sweden
CATEGORIES:Course
ORGANIZER;CN="NBIS - National Bioinformatics Infrastructure Sweden":MAILTO:education@nbis.se
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