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• W4282034018 abstract "Abstract Current biotechnologies can simultaneously measure multi-modality high-dimensional information from the same cell and tissue samples. To analyze the multi-modality data, common tasks such as joint data analysis and cross-modal prediction have been developed. However, current analytical methods are generally designed to process multi-modality data for one specific task without considering the underlying connections between tasks. Here, we present UnitedNet, a multi-task deep neural network that integrates the tasks of joint group identification and cross-modal prediction to analyze multi-modality data. We have found that multi-task learning for joint group identification and cross-modal prediction significantly improves the performance of each task. When applied to various single-cell multi-modality datasets, UnitedNet shows superior performance in each task, achieving better unsupervised and supervised joint group identification and cross-modal prediction performances compared with state-of-the-art methods. Furthermore, by considering the spatial information of cells as one modality, UnitedNet substantially improves the accuracy of tissue region identification and enables spatially resolved cross-modal prediction." @default.
• W4282034018 created "2022-06-13" @default.
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• W4282034018 date "2022-06-05" @default.
• W4282034018 modified "2023-10-16" @default.
• W4282034018 title "Multi-task learning for single-cell multi-modality biology" @default.
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• W4282034018 doi "https://doi.org/10.1101/2022.06.03.494730" @default.
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