A
Alexandre Perera-Lluna
Researcher at Polytechnic University of Catalonia
Publications - 39
Citations - 759
Alexandre Perera-Lluna is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: Cluster analysis & Biological network. The author has an hindex of 13, co-authored 36 publications receiving 481 citations. Previous affiliations of Alexandre Perera-Lluna include Hospital Sant Joan de Déu Barcelona.
Papers
More filters
Journal ArticleDOI
diffuStats: an R package to compute diffusion-based scores on biological networks.
TL;DR: The diffuStats package as discussed by the authors is an R package to compute diffusion-based scores on biological networks, which is used to compute the diffusion scores of biological networks in Bioinformatics.
Journal ArticleDOI
A new gene-based association test for genome-wide association studies
Alfonso Buil,Angel Martinez-Perez,Alexandre Perera-Lluna,Leonor Rib,Pere Caminal,José Manuel Soria +5 more
TL;DR: This work compares both strategies, SNP-based and gene-based, in a sample of cases and controls for rheumatoid arthritis, and obtained different results using each strategy, suggesting that no single strategy performs better than another in all cases.
Journal ArticleDOI
Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning
TL;DR: The results show that the lack of generalization of these models is due to a bias in public molecular databases and that a restrictive cross-validation schema based on compound clustering leads to worse but more robust and credible results.
Journal ArticleDOI
Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction.
TL;DR: This study proposes and implements four novel types of padding the amino acid sequences and analyses the impact of different ways of padding them in a hierarchical Enzyme Commission number prediction problem, showing that padding has an effect on model performance even when there are convolutional layers implied.
Journal ArticleDOI
Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins
J. A. Jaramillo-Garzon,Joan Josep Gallardo-Chacón,Joan Josep Gallardo-Chacón,César Germán Castellanos-Domínguez,Alexandre Perera-Lluna +4 more
TL;DR: An analysis of GO-slim terms predictability in plants was carried out, in order to determine single categories or groups of functions that are most related with primary structure information, to provide a valuable guide for researchers interested on further advances in protein function prediction on Embryophyta plants.