María Peña-Chilet

Unveiling the Landscape of Reportable Genetic Secondary Findings in the Spanish Population: A Comprehensive Analysis Using the Collaborative Spanish Variant Server Database

The paper analyzes genetic secondary findings in the Spanish population using the Collaborative Spanish Variant Server database, finding 5% of individuals have reportable variants in 32 ACMG-listed genes.

Characterization of the common genetic variation in the Spanish population of Navarre

The paper characterizes common genetic variants in the Navarre population, identifying 1,069 SNVs common in Navarre but rare in other populations.

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

A comprehensive and centralized database for exploring omics data in Autoimmune Diseases

ADEx is a database integrating omics data from autoimmune diseases, offering analysis tools for exploring transcriptomics and methylation studies.

Mechanistic modeling of the SARS-CoV-2 disease map

The paper presents a web interface for a mechanistic model of the SARS-CoV-2 disease map to infer gene expression-related activities and study intervention effects.

Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

This paper uses mechanistic signaling pathway models to analyze single-cell RNA-seq data from glioblastoma, revealing functional heterogeneity and drug resistance mechanisms at the single-cell level.

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

This study uses machine learning and mechanistic models to predict therapeutic targets related to Fanconi anemia, identifying over 20 potential targets.