Selected publications

Elisa G. de Lope, Saurabh Deshpande, Ramón Viñas Torné, Pietro Liò, Enrico Glaab, Stéphane P. A. Bordas (2024). Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson’s Disease. ArXiv.

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Elisa G. de Lope, Enrico Glaab (2024). funOmics: Aggregating Omics Data into Higher-Level Functional Representations. Bioconductor.

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Elisa G. de Lope, Rebecca Ting Jiin Loo, Armin Rauschenberger, Muhammad Ali, Lukas Pavelka, Tainá M. Marques, Clarissa P. C. Gomes, Rejko Krüger, Enrico Glaab On behalf of the NCER-PD Consortium (2024). Comprehensive blood metabolomics profiling of parkinson’s disease reveals coordinated alterations in Xanthine metabolism. Nature Portfolio Journal, Parkinson’s Disease (npj parkinson’s disease).

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Elisa G. de Lope, Quentin Klopfenstein (2023). NestedCV: A scikit-learn compatible class for nested cross-validation. Github.

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Elisa G. de Lope, Ramón Viñas Torné, Pietro Liò, Enrico Glaab (2023). Graph neural networks for investigating complex diseases: A case study on Parkinson's Disease. ISMB/ECCB 2023, Conference abstract.

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Elisa G. de Lope, Enrico Glaab (2022). Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease. ECCB 2022, Conference abstract.

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Ramon Diaz-Uriarte, Elisa G. de Lope, Rosalba Giugno, Holger Fröhlich, Petr V. Nazarov, Isabel A. Nepomuceno-Chamorro, Armin Rauschenberger, Enrico Glaab (2022). Ten quick tips for biomarker discovery and validation analyses using machine learning. PLOS Computational Biology.

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