📣 The CALL FOR ABSTRACTS is now open for the #bc2basel Computational Biology Conference 2025! Here's your chance to showcase your research in one of these sessions: 🔹Bioinformatics for cancer research 🔹Bioinformatics in infectious diseases 🔹Clinical data science 🔹Computational methods for single-cell and spatial omics 🔹Integrative bioinformatics for evolutionary and environmental processes 🔹Protein design and modeling molecular assemblies ⏰ Deadline: 27 February 2025 👉 Visit https://lnkd.in/eG7NYaFR to explore the sessions & get ready to shape the programme #Bioinformatics #datascience #singlecell #spatialomics #ComputationalBiology Katja Bärenfaller David Gfeller Diana Marek Abdullah Kahraman Catherine Jutzeler
SIB Swiss Institute of Bioinformatics
Forschungsdienstleistungen
The SIB Swiss Institute of Bioinformatics provides data science expertise on biological and biomedical data.
Info
The SIB Swiss Institute of Bioinformatics is an internationally recognized non-profit organization dedicated to biological and biomedical data science. It is present in the main academic institutions of Switzerland and leads numerous national and international projects with a major impact on life science research and health. SIB’s scientists are passionate about creating knowledge and converting complex questions into solutions in many fields, from biodiversity and evolution to medicine. They provide essential databases and software platforms, data management, software engineering and biocuration services, as well as computational biology know-how and training. The Institute delivers this expertise to academic groups and clinicians as well as to private companies. SIB federates the Swiss bioinformatics community of some 800 scientists, encouraging collaboration and knowledge sharing. It also cooperates with national and international institutions on research infrastructure matters. The Institute contributes to keeping Switzerland at the forefront of innovation by fostering progress in biological research and enhancing health.
- Website
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https://www.sib.swiss/?utm_source=Linkedin&utm_medium=social&utm_campaign=organic&utm_content=direct-profile
Externer Link zu SIB Swiss Institute of Bioinformatics
- Branche
- Forschungsdienstleistungen
- Größe
- 201–500 Beschäftigte
- Hauptsitz
- Geneva
- Art
- Nonprofit
- Gegründet
- 1998
- Spezialgebiete
- bioinformatics, computational biology, genomics, proteomics, glycomics, imaging, transcriptomics, phylogeny evolution, machine learning, systems biology, structural biology, Software development, Bioinformatics analysis, training, sensitive data sharing, Knowledge representation, Data stewardship, AI und Data science
Orte
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Primär
Geneva, CH
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Basel, CH
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Lausanne, CH
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Zurich, CH
Beschäftigte von SIB Swiss Institute of Bioinformatics
Updates
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🧬 Known functions are now available for over 20,000, or 82%, of human protein-coding genes, thanks to evolutionary modelling co-developed by SIB scientists and published in Nature Magazine. The innovative work overcomes gaps in experimental data on human gene functions, by integrating data from related genes in model organisms – including mice, zebrafish, fruit flies, yeast, and even plants. The resulting open resource, PAN-GO, generates clearer and more informative insights than previously available, for example when comparing genes expressed in a specific type of cancer cell to the corresponding normal cell type. See more👇 PAN-GO was developed by Marc Feuermann and Pascale Gaudet of SIB’s Swiss-Prot group in collaboration with over 150 other biologists contributing to the international Gene Ontology Consortium, including from Keck School of Medicine of the University of Southern California, University of Southern California and Phoenix Bioinformatics #OpenData #OpenScience #LifeSciences #ResearchInfrastructres
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📢 If you are a last-minute kind of person, you have still 1 week to submit your proposal for the #bc2basel conference! 💡 Theme of this year's conference: #Bioinformatics meets #AI: shaping the future of data-driven biology. 👉Send your proposal by 6 March: https://lnkd.in/eG7NYaFR
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Our director, Christophe Dessimoz signed the GBC Open Letter calling for support to sustain global biodata resources because “high-quality biodata fuels solutions to global challenges—from health to biodiversity—and enables trustworthy AI”. 👉 Read the full letter: https://lnkd.in/eya_N5K4 #BioDataCounts #BioDataResources #Health #Biodiversity #ArtificialIntelligence
Thank you to leading scientists around the world for signing the GBC’s Open Letter calling for support to sustain global biodata resources. If you haven’t already signed the letter, please do so. Click the link https://bit.ly/3UT0c6n
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Glad we had the opportunity to share our expertise in data management, interoperability, knowledge representation, data querying and LLMs at SWAT4HCLS in Barcelona this week across three different tutorial sessions. They were all packed with fully engaged participants who came to the tutorials with interesting questions and projects. Deepak Unni and Vasundra Touré led “A hands-on exploration of SPHN Semantic Interoperability Framework for FAIR knowledge graphs”. During this interactive session, participants learned how to effectively define semantics, prepare and analyze health-related data using the SPHN Semantic Interoperability Framework. Moreover, they gained a comprehensive understanding of the Swiss Personalized Health Network (SPHN) ecosystem and its role in advancing health data interoperability. Jerven Bolleman co-led “MetaData, the data you always wanted” about metadata with Andra Waagmeester showing the assembled participants how to use collected metadata about their bioinformatics resources for generating APIs, query editors and more. Vincent Emonet presented “How to efficiently apply LLMs to generate SPARQL queries over life science databases?” with preparatory support from Ana Claudia Sima and Tarcisio M. It was an exciting session on Large Language Models for scientific knowledge graphs querying. Participants learned how to deploy an LLM-based question answering system over any Knowledge Graph step-by-step. The tutorial included methods for mitigating hallucinations and an introduction to a simple agentic framework for querying knowledge graphs in natural language. Thanks again to all the people in attendance for their active participation. #HealthData #Interoperability #dataManagement #dataQuerying #Metadata #LargeLanguageModels #LLMs #SPARQL SWAT4LS
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If you are sharing or using pathogen data 🦠, please take a few minutes to answer this survey for Pathogen Data Network.
🦠 Are you sharing, or using pathogen data? Take our survey! Researchers, data stewards, bioinformaticians, healthcare professionals, policymakers, educators, and all those whose work is impacted by pathogen data are invited to take this short survey. 👉 https://lnkd.in/e4dSg5FY Results will help shape our educational initiatives and will inform the development of tools and services offered by the Pathogen Data Network. #PathogenDataNetwork #FAIRData #InfectiousDiseases #GlobalHealth #Bioinformatics #DataScience #Collaboration #OpenScience #OpenEducation #PDN
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New paper in Nature! A large group of scientists, including Tanja Stadler from SIB, discuss the opportunities and challenges of AI in infectious disease epidemiology. They consider the application of AI systems that combine machine learning, computational statistics, information retrieval and data science to the field of infectious disease epidemiology. The aim is that these thoughts support both future research in this area as well as contribute to pandemic preparedness. 👉 Read the paper: https://lnkd.in/eZzN3Sq8 #ArtificialIntelligence #MachineLearning #Epidemiology #InfectiousDiseases #PandemicPreparedness
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The database "ModelArchive: a deposition database for computational macromolecular structural models" by Gerardo Tauriello, Andrew M. Waterhouse, Juergen Haas, Dario Behringer, Stefan Bienert, Thomas Garello, and Torsten Schwede was just published in the Journal of Molecular Biology 👉 https://lnkd.in/e2VH7V7z 💡 ModelArchive is a deposition database for computational macromolecular, complementing the Protein Data Bank and AlphaFold DB. It hosts 600,000+ models, supporting various molecules and complexes. #Bioinformatics #StructuralBiology #ComputationalBiology #ProteinModeling #Macromolecules #StructuralPrediction
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🚨 Deadline extended! 🚨 You now have until Thursday 6 March to submit your abstract for the #bc2basel conference. Don't miss this chance to share your research and insights with the bioinformatics community. 👉 Submit your abstract here: https://lnkd.in/eG7NYaFR 💡 Don't miss the opportunity to be part of Switzerland's premier computational biology event, featuring inspiring keynotes (Serena Nik-Zainal & Peer Bork), thematic tracks, and a session on transitioning AI-driven projects into successful start-ups. Connect with over 500 scientists and innovators from academia, industry, and healthcare. #Bioinformatics #ComputationalBiology #CallForAbstracts
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Discover this preprint: It uses a deep-learning model to effectively predict spatial transcriptomics directly from H&E images, paving the way for more accessible molecular insights into cancer tissues. #CancerResearch #SpatialTranscriptomics #Bioinformatics
🎉 Excited to share our work on predicting spatial transcriptomics from H&E images using deep learning! Spatial transcriptomics offers invaluable insights into the molecular landscapes of tissues, but its cost and complexity limit its use in clinical workflows. To address this, we developed a deep learning model trained on 10x Genomics Visium that leverages pathology foundation models and spatial tissue context to effectively predict spatial transcriptomics directly from H&E images. 🚀 Using this approach, we’ve generated the largest public spatial transcriptomics dataset - over 37 million spots from 1 792 TCGA slides across skin melanoma and kidney cancer. This dataset enriches the available spatial transcriptomics data for TCGA samples, facilitates molecular insights into cancer tissues, and showcases a path toward affordable integration into clinical workflows. 🔍Read our pre-print at: https://lnkd.in/dB93KyvA 💻Code: https://lnkd.in/dRsNqMbv 🤗TCGA data: https://lnkd.in/dXT5hzMd With Sebastian Dawo Karīna Siliņa, Holger Moch, Sonali Andani, Tumor Profiler Consortium, Viktor H. Koelzer and Gunnar Rätsch This project was carried out in close partnership between the Biomedical Informatics Group at ETH Zurich, the Computational and Translational Pathology Lab at the University of Zurich and the University of Basel, and the Silina Group at the Institute of Pharmaceutical Sciences, ETH Zurich - many thanks for the great collaboration! ETH Zürich Department of Computer Science (D-INFK), ETH Zürich Universitätsspital Zürich University of Zurich University of Basel SIB Swiss Institute of Bioinformatics #MachineLearning #SpatialTranscriptomics #Pathology
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