Adèle H. Ribeiro
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Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering Open
Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependencies arisen from unobserved confounding. C-DAGs define an eq…
View article: Soft Drink Consumption and Depression Mediated by Gut Microbiome Alterations
Soft Drink Consumption and Depression Mediated by Gut Microbiome Alterations Open
Importance Soft drink consumption is linked to negative physical and mental health outcomes, but its association with major depressive disorder (MDD) and the underlying mechanisms remains unclear. Objective To examine the association betwe…
View article: From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference
From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference Open
With an estimated 263 million cases recorded worldwide in 2023, malaria remains a major global health challenge, particularly in tropical regions with limited healthcare access. Beyond its health impact, malaria disrupts education, economi…
View article: dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data
dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data Open
Causal discovery is central to inferring causal relationships from observational data. In the presence of latent confounding, algorithms such as Fast Causal Inference (FCI) learn a Partial Ancestral Graph (PAG) representing the true model'…
View article: AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways
AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways Open
Introduction Cardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors an…
Artificial Intelligence-Driven Screening System for Rapid Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization Open
The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as Atrial Fibrillation and Myocardial Infarction. A prompt and efficient assessment of ECG exams…
Artificial Intelligence-Driven Screening System for Rapid Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization Open
The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as Atrial Fibrillation and Myocardial Infarction. A prompt and efficient assessment of ECG exams…
Sharing Data With Shared Benefits: Artificial Intelligence Perspective Open
Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reason…
Causal Effect Identification in Cluster DAGs Open
Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been developed for performing such tasks in the last decades. O…
Sharing Data With Shared Benefits: Artificial Intelligence Perspective (Preprint) Open
UNSTRUCTURED Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to v…
Artificial Intelligence-Driven Screening System for Rapid Image-Based Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization Open
The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as atrial fibrillation and myocardial infarction. A prompt and efficient assessment of ECG exams…
Causal Effect Identification in Cluster DAGs Open
Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been developed for performing such tasks in the last decades (P…
Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder Open
Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understan…
Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments Open
Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity valu…
Identification of causality in genetics and neuroscience Open
\n Causal inference may help us to understand the underlying mechanisms and the risk factors of diseases. In Genetics, it is crucial to understand how the connectivity among variables is influenced by genetic and environmental factors. Fam…