72 citations · 75 across the 5 of their papers we have counts for
5 papers
WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets
Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez, Carlos Martínez-Cortés
While predictive accuracy is often prioritized in machine learning (ML) models, interpretability remains essential in scientific and high-stakes domains. However, diverse interpret…
Performance Impact of Containerized METADOCK 2 on Heterogeneous Platforms
Antonio Jesús Banegas-Luna, Baldomero Imbernón Tudela, Carlos Martínez-Cortés +2
Virtual screening (VS) is a computationally intensive process crucial for drug discovery, often requiring significant resources to analyze large chemical libraries and predict liga…
Fighting the disagreement in Explainable Machine Learning with consensus
Antonio Jesus Banegas-Luna, Carlos Martınez-Cortes, Horacio Perez-Sanchez
Machine learning (ML) models are often valued by the accuracy of their predictions. However, in some areas of science, the inner workings of models are as relevant as their accurac…
SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts
Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez
Personalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible al- ternative for predicting the mos…
When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey
Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene +6
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. In a few decades, computers may be capable of formulating diagnoses…