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Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

, , , , , , and . Comput. Biol. Medicine, (September 2023)

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Kernel Wasserstein Distance., , , , , and . CoRR, (2019)The maximum entropy principle for compositional data., , , and . BMC Bioinform., 23 (1): 449 (2022)Optimal Transport for Kernel Gaussian Mixture Models., , , , , and . CoRR, (2023)Predicting Local Failure in Lung Cancer Using Bayesian Networks., , , , , , , and . ICMLA, page 735-739. IEEE Computer Society, (2010)Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation., , , , , , , and . MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 777-785. Springer, (2018)Modeling the Relationship between Fluorodeoxyglucose Uptake and Tumor Radioresistance as a Function of the Tumor Microenvironment., and . Comput. Math. Methods Medicine, (2014)A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer., and . Briefings Bioinform., 17 (3): 468-478 (2016)Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy., , , , , , , , , and 4 other author(s). INFORMS J. Appl. Anal., 52 (1): 69-89 (2022)A novel kernel Wasserstein distance on Gaussian measures: An application of identifying dental artifacts in head and neck computed tomography., , , , , and . Comput. Biol. Medicine, (2020)Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images., , , , , , , and . IEEE Trans. Medical Imaging, 38 (1): 134-144 (2019)