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CustOmics: A versatile deep-learning based strategy for multi-omics integration., , , и . PLoS Comput. Biol., (марта 2023)Neural Networks to Predict Survival from RNA-seq Data in Oncology., , и . CMSB, том 12881 из Lecture Notes in Computer Science, стр. 122-140. Springer, (2021)Learning from User Workflows for the Characterization and Prediction of Software Crashes., , и . ICDM Workshops, стр. 1023-1030. IEEE Computer Society, (2016)Leveraging Conditional Generative Models in a General Explanation Framework of Classifier Decisions., , , и . CoRR, (2021)Reachability Design Through Approximate Bayesian Computation., , и . CMSB, том 11773 из Lecture Notes in Computer Science, стр. 207-223. Springer, (2019)Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions., , , и . iMIMIC/TDA4MedicalData@MICCAI, том 12929 из Lecture Notes in Computer Science, стр. 44-55. Springer, (2021)Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction., , и . SIAM Conf. on Control and its Applications, стр. 67-74. SIAM, (2013)A biology-driven deep generative model for cell-type annotation in cytometry., , , , , , и . Briefings Bioinform., (сентября 2023)CustOmics: A versatile deep-learning based strategy for multi-omics integration., , , и . CoRR, (2022)Interpretable cytometry cell-type annotation with flow-based deep generative models., , , , , , и . CoRR, (2022)