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Apr 4, 2020 · In this paper, we compare the performance of both generative and discriminative deep models based on their integration stage.
Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity ...
Jun 28, 2022 · We demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
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This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).
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A Brief Review on Deep Learning Applications in Genomic Studies
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In this paper, we provide a brief review on deep learning techniques and various applications of deep learning to genomic studies.
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May 2, 2024 · This review delves into the recent advancements in GNNs and Transformers for the fusion of multimodal data in oncology, spotlighting key studies and their ...
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Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis.
Aug 31, 2023 · We conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration.
Jul 25, 2022 · In the current review, we address development and application of deep learning methods/models in different subarea of human genomics.
Aug 29, 2022 · Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response.