Percorrer por autor "Costa, Felipe Xavier"
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- Quantifying edge relevance for epidemic spreading via the semi-metric topology of complex networksPublication . Soriano-Paños, David; Costa, Felipe Xavier; Rocha, Luis M.Sparsification aims at extracting a reduced core of associations that best preserves both the dynamics and topology of networks while reducing the computational cost of simulations. We show that the semi-metric topology of complex networks yields a natural and algebraically-principled sparsification that outperforms existing methods on those goals. Weighted graphs whose edges represent distances between nodes are \textit{semi-metric} when at least one edge breaks the triangle inequality (transitivity). We first confirm with new experiments that the \textit{metric backbone}—a unique subgraph of all edges that obey the triangle inequality and thus preserve all shortest paths—recovers Susceptible-Infected dynamics over the original non-sparsified graph. This recovery is improved when we remove only those edges that break the triangle inequality significantly, i.e., edges with large semi-metric distortion. Based on these results, we propose the new \textit{semi-metric distortion sparsification} method to progressively sparsify networks in decreasing order of semi-metric distortion. Our method recovers the macro- and micro-level dynamics of epidemic outbreaks better than other methods while also yielding sparser yet connected subgraphs that preserve all shortest paths. Overall, we show that semi-metric distortion overcomes the limitations of edge betweenness in ranking the dynamical relevance of edges not participating in any shortest path, as it quantifies the existence and strength of alternative transmission pathways.
- Sex-specific transcriptome similarity networks elucidate comorbidity relationshipsPublication . Sánchez-Valle, Jon; Flores-Rodero, María; Costa, Felipe Xavier; Carbonell-Caballero, Jose; Núñez-Carpintero, Iker; Tabarés-Seisdedos, Rafael; Rocha, Luis Mateus; Cirillo, Davide; Valencia, AlfonsoHumans present sex-driven biological differences. Consequently, the prevalence of analyzing specific diseases and comorbidities differs between the sexes, directly impacting patients’ management and treatment. Despite its relevance and the growing evidence of said differences across numerous diseases (with 4,370 PubMed results published within the past year), knowledge at the comorbidity level remains limited. In fact, to date, no study has attempted to identify the biological processes altered differently in women and men, promoting differences in comorbidities. To shed light on this problem, we analyze expression data for more than 100 diseases from public repositories, analyzing each sex independently. We calculate similarities between differential expression profiles by disease pairs and find that 13-16% of transcriptomically similar disease pairs are sex-specific. By comparing these results with epidemiological evidence, we recapitulate 53-60% of known comorbidities distinctly described for men and women, finding sex-specific transcriptomic similarities between sex-specific comorbid diseases. The analysis of shared underlying pathways shows that diseases can co-occur in men and women by altering alternative biological processes. Finally, we identify different drugs differentially associated with comorbid diseases depending on patients’ sex, highlighting the need to consider this relevant variable in the administration of drugs due to their possible influence on comorbidities.
- Sex-specific transcriptome similarity networks elucidate comorbidity relationshipsPublication . Sánchez-Valle, Jon; Flores-Rodero, María; Costa, Felipe Xavier; Carbonell-Caballero, Jose; Núñez-Carpintero, Iker; Tabarés-Seisdedos, Rafael; Rocha, Luis Mateus; Cirillo, Davide; Valencia, AlfonsoBackground Biological differences between women and men lead to variations in the prevalence and progression of many diseases, influencing diagnosis, management, and treatment outcomes. However, the biological mechanisms that contribute to sex differences in disease co-occurrence remain largely unexplored. This study aims to uncover the molecular processes underlying sex-specific patterns of comorbidity. Methods We analyze gene expression data from over 100 diseases, considering the biological sex of each sample (8906 samples, 43.06% women). For each sex, we construct disease similarity networks based on differential gene expression profiles and identify enriched biological processes. We then compare these networks with epidemiological data from population-level comorbidity studies to assess their concordance. Finally, we investigate drugs associated with sex-specific comorbidities to identify potential differences in therapeutic response. Results We show that 13–16% of transcriptomically similar disease pairs are sex-specific. These similarities recover 53–60% of known comorbidities that differ between women and men. Diseases can co-occur through the differential alteration of biological processes, with immune and metabolic pathways playing a greater role in women, and extracellular matrix organization and signal transduction pathways in men. We also identify drugs differentially linked to comorbid diseases depending on sex, suggesting possible sex-dependent effects on disease co-occurrence. Conclusions Our findings demonstrate that transcriptomic data can reveal sex-specific molecular links between diseases and suggest that biological sex should be considered in the design of therapeutic strategies and drug administration.