🏵️Computational Approaches for Single-Cell Data Analysis plus AI Expansion

Single-cell technologies reveal cellular heterogeneity, requiring advanced computational analysis. High-dimensional data processing using dimensionality reduction and clustering methods like tSNE and PCA helps visualize cell populations. Disease modeling, such as in myeloma and demyelination, uses single-cell data to understand cellular dynamics and identify therapeutic targets. Neurobiology and vascular studies utilize these techniques to analyze complex processes like blood-brain barrier dysfunction. Statistical frameworks, including hypothesis testing and Bayesian models, ensure rigorous analysis. Machine learning, like logistic regression, builds predictive models. Computational approaches are crucial for translating single-cell data into biological insights, advancing medicine and biology.