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This review highlights recent advances in single-cell \u201comics\u201d data analysis and discusses their applicability for brain immunology. Traditional statistical techniques, adapted for single-cell omics, have been crucial in categorizing cell types and identifying gene signatures, overcoming challenges posed by increasingly complex datasets. We explore how machine learning, particularly deep learning methods like autoencoders and graph neural networks, is addressing these challenges by enhancing dimensionality reduction, data integration, and feature extraction. Newly developed foundation models present exciting opportunities for uncovering gene expression programs and predicting genetic perturbations. Focusing on brain development, we demonstrate how single-cell analyses have resolved immune cell heterogeneity, identified temporal maturation trajectories, and uncovered potential therapeutic links to various pathologies, including brain malignancies and neurodegeneration. The integration of single-cell and spatial omics has elucidated the intricate cellular interplay within the developing brain. This mini-review is intended for wet lab biologists at all career stages, offering a concise overview of the evolving landscape of single-cell omics in the age of widely available artificial intelligence.<\/jats:p>","DOI":"10.3389\/fbinf.2025.1554010","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T05:23:51Z","timestamp":1744867431000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Primer on machine learning applications in brain immunology"],"prefix":"10.3389","volume":"5","author":[{"given":"Niklas","family":"Binder","sequence":"first","affiliation":[]},{"given":"Ashkan","family":"Khavaran","sequence":"additional","affiliation":[]},{"given":"Roman","family":"Sankowski","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1038\/s41592-023-01909-9","article-title":"MultiVI: deep generative model for the integration of multimodal data","volume":"20","author":"Ashuach","year":"2023","journal-title":"Nat. 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