Biology subjects

Llabres, M.

Publications and source records attributed to Llabres, M..

3 recordsLinked to original sources

bioRxiv2026

VPF-Class 2.0: a taxonomy-centered framework for automatic viral classification

Rapid expansion of viral sequence data demands classifiers that scale, track ICTV updates, and provide interpretable evidence. We present VPF-Class 2.0, an updated successor to VPF-Class, centred on the taxonomic classification, that retains marker-driven protein domain detection but replaces rule-based voting with a lightweight supervised model on per-genome marker-composition features. In controlled benchmarks, VPF-Class 2.0 achieves near-perfect family-level performance and strong genus-level accuracy while increasing confident annotation coverage. Under a practical confidence threshold (0.3), performance improves and matches or exceeds representative tools within shared taxonomic scopes. We further introduce an interpretability study that relates errors to the genus specificity of activated markers. Finally, we demonstrate applicability on large real-world viromes with consistent labels and substantial agreement with graph-based classifications. The implementation of VPF-Class 2.0 can be downloaded from https://github.com/luisvidalj/VPFClass2.git.

bioinformatics↗

bioRxiv2020

Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2

Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last few months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.View Full Text

bioinformatics↗

bioRxiv2019

AligNet: Alignment of Protein-Protein Interaction Networks

One of the most difficult problems difficult problem in systems biology is to discover protein-protein interactions as well as their associated functions. The analysis and alignment of protein-protein interaction networks (PPIN), which are the standard model to describe protein-protein interactions, has become a key ingredient to obtain functional orthologs as well as evolutionary conserved pathways and protein complexes. Several methods have been proposed to solve the PPIN alignment problem, aimed to match conserved subnetworks or functionally related proteins. However, the right balance between considering network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm which, unfortunately, remains unsolved. Therefore, in this work, we propose AligNet, a new method and software tool for the pairwise global alignment of PPIN that produces biologically meaningful alignments and more efficient computations than state-of-the-art methods and tools, by achieving a good balance between structural matching and protein function conservation as well as reasonable running times.

systems biology↗