YouTube video by BabrahamBioinf

This release of the Human Disease Ontology includes 11,985 disease classes, 9,698 with textual definitions (80.9%). Translation files now include 11,582 labels (96.6%), 11,490 synonyms (59.2%), and...

Join a one-day, hands-on workshop on AI-powered image analysis led by Nikolay Oskolkov (Group Leader/PI, LIOS) designed for PhD students, professors, and professional researchers who need to turn microscopy and clinical images into reproducible, publication-ready measurements. You’ll get rigorous theory plus live coding in Jupyter on Linux, ChatGPT-assisted Python notebooks, and practical end-to-end pipelines covering CNNs, transfer learning, UNet and Mask R-CNN segmentation, object detection, classical OpenCV preprocessing, uncertainty quantification, model interpretability (GRAD-CAM), and best practices for data management, evaluation, and reproducible reporting. By the end of the workshop you will be able to build, train and evaluate segmentation and detection models on real biological datasets, apply augmentation and fine-tuning for small datasets, quantify and interpret uncertainty, and package analyses into reusable Jupyter workflows suitable for theses and peer‑reviewed papers. Live sessions are streamed via Zoom, all recordings and materials are available for 30 days after the seminar, a monitored Q&A forum will be open for follow-up questions, and participants receive an Instats certificate of completion (ECTS-equivalent points shown for European students where applicable). Register or contact us to learn more and reserve your spot. #ComputationalBiology #DataAnalytics #DataScience #MachineLearning #BiomedicalEngineering #Bioinformatics #ComputerScience #ChatGPT #Jupyter #Linux #Python #R #Research #ResearchTraining #Instats

ML based AI tool for personalised medicine utilising multi-omics data - Aridoge13/Kabosu

Master the rapidly growing field of single-cell analysis in a one-day, hands-on workshop led by Nikolay Oskolkov (Molecular Biosciences, Lund University) designed for PhD students, academics and research professionals: learn to process and interpret single-cell RNA‑seq data using R and Python (Seurat and Scanpy), covering QC and normalization, dimensionality reduction, clustering and visualization, differential expression, dataset integration and biological interpretation through practical case studies. The course gives you a complete pipeline from raw data to insight, includes take‑home challenges, and is delivered live via Zoom (recordings, code, data and slides available for 30 days) — or on-demand with 30 days access after enrollment — with an instructor‑monitored forum for 30 days post‑seminar. All participants receive an Instats certificate of completion (ECTS equivalent indicated for European students). Register or contact us to secure your place and boost your single-cell analysis skills. #Biostatistics #ComputerScience #DataScience #Biology #Bioinformatics #ComputationalBiology #Biomedicine #Genomics #Statistics #MolecularBiology #R #Python #Research #ResearchTraining #Instats

Scholars perform cutting-edge, collaborative, and multidisciplinary computational science aimed at solving fundamental biological questions.