The advent of single-cell technology has revolutionized the field of biomedicine, enabling researchers to investigate tissue composition at the individual cell level and decipher the distinct functions of various cell types. As this technology continues to advance, it is generating vast amounts of data, which can be leveraged to elucidate the effects of diseases, such as lung cancer or COVID-19, on cellular structures. To harness the full potential of these datasets, researchers are turning to machine learning methods, particularly self-supervised learning, to identify patterns and derive meaningful insights.
A recent study published in Nature Machine Intelligence has demonstrated the efficacy of self-supervised learning in analyzing large single-cell datasets, showcasing its ability to improve performance in tasks such as predicting cell types and reconstructing gene expression, and paving the way for the development of virtual cells - comprehensive computer models that can replicate the diversity of cells in different datasets, holding promise for the analysis of cellular changes associated with diseases.
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