Nonlinear physics opens a new paradigm for accurate transcription start site prediction. - José Antonio Barbero-Aparicio; Santiago Cuesta-López; César Ignacio García-Osorio; Javier Pérez-Rodríguez; Nicolás García-Pedrajas (BMC Bioinform., 2022)

Deep learning and support vector machines for transcription start site identification. - José Antonio Barbero-Aparicio; Alicia Olivares-Gil; José-Francisco Díez-Pastor; César García-Osorio (PeerJ Comput. Sci., 2023)

CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. - Irene Ortega-Sanz; José Antonio Barbero-Aparicio; Antonio Canepa-Oneto; Jordi Rovira; Beatriz Melero (BMC Bioinform., 2023)

Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques. - José Antonio Barbero-Aparicio; Alicia Olivares-Gil; Juan José Rodríguez; César García-Osorio; José-Francisco Díez-Pastor (Inf. Fusion, 2024)

An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data. - Ismael Ramos-Pérez; José Antonio Barbero-Aparicio; Antonio Canepa-Oneto; Álvar Arnaiz-González; Jesús Manuel Maudes Raedo (Inf., 2024)

Unveiling the Differences in Early Performance Prediction Between Online Social Sciences and STEM Courses Using Educational Data Mining. - Raúl Marticorena Sánchez; Antonio Canepa-Oneto; Carlos López Nozal; José Antonio Barbero-Aparicio (Expert Syst. J. Knowl. Eng., 2025)

Semi-supervised prediction of protein fitness for data-driven protein engineering. - Alicia Olivares-Gil; José Antonio Barbero-Aparicio; Juan José Rodríguez; José-Francisco Díez-Pastor; César García-Osorio; Mehdi D. Davari (J. Cheminformatics, 2025)