Hi everyone
Instats is excited to offer a 1-day seminar, AI-Powered multiOmics Data Analysis, livestreaming on 25 September and led by Dr Nikolay Oskolkov, Group Leader (PI) at LIOS. Multi-omics datasets—combining genomics, transcriptomics, proteomics, metabolomics and single-cell measurements—promise transformative insight into complex biological systems, yet their high dimensionality, noise and heterogeneity demand sophisticated analytical approaches. This intensive workshop delivers an end-to-end roadmap: rigorous preprocessing, imputation and batch correction; unsupervised integration with factor analysis, PLS and MOFA; supervised multimodal prediction and objective evaluation on independent cohorts; and hands-on deep learning workflows in TensorFlow. Through concise lectures, case studies and coding sessions in Python and RStudio—supported by advanced LLMs for streamlined scripting—you’ll build reproducible notebooks and flexible pipelines for bulk, single-cell and longitudinal datasets. By the end of the day, participants will be able to design robust analysis strategies, train interpretable models and confidently translate multi-omics data into testable biological hypotheses.
Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!
Best wishes
Michael Zyphur
Professor and Director
Instats | instats.org
Instats is excited to offer a 1-day seminar, AI-Powered multiOmics Data Analysis, livestreaming on 25 September and led by Dr Nikolay Oskolkov, Group Leader (PI) at LIOS. Multi-omics datasets—combining genomics, transcriptomics, proteomics, metabolomics and single-cell measurements—promise transformative insight into complex biological systems, yet their high dimensionality, noise and heterogeneity demand sophisticated analytical approaches. This intensive workshop delivers an end-to-end roadmap: rigorous preprocessing, imputation and batch correction; unsupervised integration with factor analysis, PLS and MOFA; supervised multimodal prediction and objective evaluation on independent cohorts; and hands-on deep learning workflows in TensorFlow. Through concise lectures, case studies and coding sessions in Python and RStudio—supported by advanced LLMs for streamlined scripting—you’ll build reproducible notebooks and flexible pipelines for bulk, single-cell and longitudinal datasets. By the end of the day, participants will be able to design robust analysis strategies, train interpretable models and confidently translate multi-omics data into testable biological hypotheses.
Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!
Best wishes
Michael Zyphur
Professor and Director
Instats | instats.org
