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11 months ago

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Joint variational autoencoders for multimodal imputation and embedding - Nature Machine Intelligence

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Joint variational autoencoders for multimodal imputation and embedding - Nature Machine Intelligence

Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity.

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Joint variational autoencoders for multimodal imputation and embedding - Nature Machine Intelligence

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Joint variational autoencoders for multimodal imputation and embedding - Nature Machine Intelligence

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