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pro vyhledávání: '"Torne, Ramon Vinas"'
Autor:
de Lope, Elisa Gómez, Deshpande, Saurabh, Torné, Ramón Viñas, Liò, Pietro, Glaab, Enrico, Bordas, Stéphane P. A.
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neural networks have emerged as promising alternatives, yet the optimal st
Externí odkaz:
http://arxiv.org/abs/2406.14442
Autor:
Shadbahr, Tolou, Roberts, Michael, Stanczuk, Jan, Gilbey, Julian, Teare, Philip, Dittmer, Sören, Thorpe, Matthew, Torne, Ramon Vinas, Sala, Evis, Lio, Pietro, Patel, Mishal, Collaboration, AIX-COVNET, Rudd, James H. F., Mirtti, Tuomas, Rannikko, Antti, Aston, John A. D., Tang, Jing, Schönlieb, Carola-Bibiane
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by
Externí odkaz:
http://arxiv.org/abs/2206.08478
There have been several successful deep learning models that perform audio super-resolution. Many of these approaches involve using preprocessed feature extraction which requires a lot of domain-specific signal processing knowledge to implement. Conv
Externí odkaz:
http://arxiv.org/abs/2109.14994
Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Ge
Externí odkaz:
http://arxiv.org/abs/2107.11856
Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients. The aim of this work is to present h
Externí odkaz:
http://arxiv.org/abs/2009.08299
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