Description:
RNA biology has become an increasingly data-rich and high-dimensional field, creating a growing need for computational and machine-learning approaches to extract biological insight from large-scale datasets. Machine learning (ML) provides a powerful set of tools for learning patterns from high-dimensional biological data, enabling both predictive modelling and the generation of biologically meaningful hypotheses. In this workshop, participants will gain a solid foundation in machine learning theory and its applications to RNA biology. Participants will be introduced to the core concepts underlying machine learning and deep learning. The course will combine conceptual understanding with hands-on practice, guiding participants through an end-to-end workflow. Covered topics include how to retrieve data from online resources, automate repetitive collection tasks, prepare datasets for machine learning, build machine learning models and evaluate their performance, and explore how pretrained RNA encoders generate embeddings that can be used for downstream bioinformatics tasks.
Dates and Times
Noon to 3:00 pm EST on the following days:
Prerequisites
Participants are expected to have a basic working knowledge of Python and prior experience with introductory data analysis, such as attending prior RNA Canada workshops “Introduction to Python: Sequence-to-Function Analysis for RNA” or “Python for Bioinformatics: Computational Analysis and Modelling of RNA Structure”.
More details here:
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