Deep Learning for Evolutionary Genomics (DLEG01) – Applications for Marine Mammal Research
Deep Learning for Evolutionary Genomics (DLEG01)<https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01?utm_source=chatgpt.com> Delivered by Dr. Nikolay Oskolkov, a bioinformatician, computational biologist, and data scientist with more than 20 years of teaching experience. His expertise spans machine learning, deep learning, population genomics, metagenomics, microbiome research, environmental and ancient DNA, dimensionality reduction, and computational biology. Learn how to apply deep learning and neural networks to complex biological and genomic datasets, with approaches relevant to marine mammal genomics, population genetics, conservation biology, environmental DNA, microbiome research, and evolutionary studies. Marine mammal research increasingly generates large and high-dimensional datasets, from population genomic and environmental DNA data to microbiome and functional genomic datasets. Deep learning provides powerful tools for extracting patterns from these datasets, including genomic classification, population-level representation learning, functional annotation, biological sequence analysis, and environmental source attribution. What you'll gain * An understanding of the theoretical foundations of artificial neural networks and deep learning * Practical experience implementing deep-learning methods in R and Python * Understanding of feed-forward neural networks, CNNs, RNNs, and LSTM networks * Applications of deep learning to population genomics and representation learning * Use of autoencoders for dimensionality reduction, with comparison to PCA, t-SNE and UMAP * Approaches for identifying introgressed genomic regions using neural networks * Applications of CNNs to gene annotation and functional genomic elements * Understanding of microbial ecology and environmental metagenomics * Use of CNNs for microbiome source tracking and environmental source attribution * Approaches for analysing DNA and other biological sequences * Introduction to transformer architectures for biological sequence modelling * Applications to ancient and environmental DNA research Course format * 5-day live, instructor-led online course * 25 hours of training * Combination of lectures and hands-on practical exercises * Practical implementation in R and Python * Examples from ecology, evolutionary biology, population genomics, metagenomics and microbiome research * Opportunity to discuss participants' own data * All code, datasets and presentation materials provided * All live sessions recorded * 30 days of recording access and post-course email support Who is this course for? * Marine mammal researchers interested in genomics and AI * Cetacean and pinniped researchers * Marine molecular and evolutionary ecologists * Conservation geneticists and genomic researchers * Researchers working with population genomic data * Marine microbiome and metagenomics researchers * Environmental DNA researchers * Bioinformaticians and computational biologists * Postgraduate students and early-career researchers Participants should have a basic background in R or Python and some familiarity with biological datasets. A foundation in statistics, probability and basic machine learning is useful but not required, and no previous experience with neural networks or deep learning is necessary. Why take this course? Genomic and molecular approaches are becoming increasingly important in marine mammal science, particularly for investigating population structure, genetic variation, connectivity, evolutionary history, conservation genetics and responses to environmental change. As these datasets increase in size and complexity, machine learning and deep learning offer new ways to extract biologically meaningful patterns. The course's coverage of population genomics and representation learning is particularly relevant to researchers working with genomic data from cetacean and pinniped populations. Autoencoder neural networks provide approaches for reducing the dimensionality of complex genomic datasets, while neural-network methods can be used to identify patterns such as introgressed genomic regions. There are also applications beyond population genomics. Environmental metagenomics and microbiome source tracking can be relevant to studies of marine mammal microbiomes, health and environmental interactions, while CNN-based functional genomics and biological sequence modelling provide tools for extracting information from genomic sequences. The course also introduces approaches relevant to environmental and ancient DNA. Whether you're investigating marine mammal population genomics, conservation genetics, introgression, environmental DNA, microbiomes, functional genomics, or other high-dimensional molecular datasets, this course provides a practical foundation for incorporating modern deep-learning methods into marine mammal research. Course dates 5–9 October 2026 25 hours | Live online | £400 Learn more & enrol PR Stats – Deep Learning for Evolutionary Genomics (DLEG01)<https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01?utm_source=chatgpt.com> Questions? 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