2 citations · 4 across the 14 of their papers we have counts for
16 papers
Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry
Alejandro L. García-Navarro, Carlos Sevilla-Salcedo, Belén Rodríguez-Sánchez +1
Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistanc…
Bayesian Integration of Nonlinear Incomplete Clinical Data
Lucía González-Zamorano, Nuria Balbás-Esteban, Vanessa Gómez-Verdejo +2
Multimodal clinical data are characterized by high dimensionality, heterogeneous representations, and structured missingness, posing significant challenges for predictive modeling,…
AI-driven Generation of MALDI-TOF MS for Microbial Characterization
Lucía Schmidt-Santiago, David Rodríguez-Temporal, Carlos Sevilla-Salcedo +1
Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become a cornerstone technology in clinical microbiology, enabling rapid and accurat…
Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Janaina Mourao-Miranda +1
Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limi…
CLARAE: Clarity Preserving Reconstruction AutoEncoder for Denoising and Rhythm Classification of Intracardiac Electrograms
Long Lin, Pablo Peiro-Corbacho, Pablo Ávila +4
Intracavitary atrial electrograms (EGMs) provide high-resolution insights into cardiac electrophysiology but are often contaminated by noise and remain high-dimensional, limiting r…
Latent Representations of Intracardiac Electrograms for Atrial Fibrillation Driver Detection
Pablo Peiro-Corbacho, Long Lin, Pablo Ávila +4
Atrial Fibrillation (AF) is the most prevalent sustained arrhythmia, yet current ablation therapies, including pulmonary vein isolation, are frequently ineffective in persistent AF…
Virtual Laboratories: Domain-agnostic workflows for research
Carlos Sevilla-Salcedo, Armi Tiihonen, Mahsa Asadi +4
Many scientific disciplines have traditionally advanced by iterating over hypotheses using labor-intensive trial-and-error, which is a slow and expensive process. Recent advances i…
Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Jussi Tohka +1
We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions.…
The Relevance Feature and Vector Machine for health applications
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Emilio Parrado-Hernández +1
This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective stud…
Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Vandad Imani, Elaheh Moradi, Carlos Sevilla-Salcedo +2
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Gen…
Multi-Objective Genetic Algorithm for Multi-View Feature Selection
Vandad Imani, Carlos Sevilla-Salcedo, Elaheh Moradi +2
Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase…
Bayesian learning of feature spaces for multitasks problems
Carlos Sevilla-Salcedo, Ascensión Gallardo-Antolín, Vanessa Gómez-Verdejo +1
This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fou…
Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space
Alejandro Guerrero-López, Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo +1
Purpose: Handling heterogeneous and mixed data types has become increasingly critical with the exponential growth in real-world databases. While deep generative models attempt to m…
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos +2
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missin…
Bayesian Sparse Factor Analysis with Kernelized Observations
Carlos Sevilla-Salcedo, Alejandro Guerrero-López, Pablo M. Olmos +1
Multi-view problems can be faced with latent variable models since they are able to find low-dimensional projections that fairly capture the correlations among the multiple views t…
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis
Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo, Pablo M. Olmos
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate s…