Research

Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson’s Disease
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical …
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Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson’s Disease
funOmics: Aggregating Omics Data into Higher-Level Functional Representations
The funOmics R package is a collection of functions for aggregating omics data into higher-level functional representations such as …
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funOmics: Aggregating Omics Data into Higher-Level Functional Representations
Comprehensive blood metabolomics profiling of parkinson’s disease reveals coordinated alterations in Xanthine metabolism
Parkinson’s disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective …
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Comprehensive blood metabolomics profiling of parkinson’s disease reveals coordinated alterations in Xanthine metabolism
NestedCV: A scikit-learn compatible class for nested cross-validation
NestedCV() is a python class compatible with scikit-learn extensions. It provides functions that streamline the nested cross-validation …
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NestedCV: A scikit-learn compatible class for nested cross-validation
Graph neural networks for investigating complex diseases: A case study on Parkinson's Disease
Omics data analysis is a critical component in the study of complex diseases, but the high dimension and heterogeneity of the data …
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Graph neural networks for investigating complex diseases: A case study on Parkinson's Disease
Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease
Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, …
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Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease
Ten quick tips for biomarker discovery and validation analyses using machine learning
High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample …
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Ten quick tips for biomarker discovery and validation analyses using machine learning