Julio Sáez-Rodríguez

The paper uses a kinetic model of central carbon metabolism to predict metabolic reprogramming in liver cancer and validates findings through experiments.

CARNIVAL identifies upstream causal processes from gene expression data by integrating prior knowledge, improving accuracy over existing tools in IgA nephropathy analysis.

This paper introduces SVCA, a method to analyze spatial molecular data and quantify the impact of cell-cell interactions on gene expression in breast cancer tissue.

The paper tests if human pathway and TF footprints can be applied to mouse data, showing they recover mouse perturbations and disease associations.

The paper links perturbation transcriptomics data with cell viability phenotypes to identify cell death and proliferation signatures, improving drug sensitivity prediction.

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

The paper benchmarks and integrates TF regulon resources to assess their impact on TF activity estimation in human gene expression data.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

CRISPRcleanR reduces false positives in CRISPR-Cas9 screens by correcting gene-independent responses without assuming copy number status.

The paper uses multi-task learning to link drug targets and pathway activation, revealing interactions that guide tissue-specific combination therapies.

The paper analyzes the human kinome and phosphatome to identify signaling networks altered by protein overexpression, revealing novel relationships and mechanisms of cancer progression.

This study quantifies genomic, proteomic, and phenotypic variability in HeLa cells across laboratories and passages, revealing implications for research reproducibility.

The paper develops SLAPenrich to analyze pathway alterations in cancer hallmarks across 4,415 patients, revealing hallmark predominance and suggesting novel driver genes.

The paper systematically analyzes chaperome deregulation across 22 solid cancers using gene expression data and develops a tool to visualize proteostasis alterations.

The paper identifies phosphorylation sites in metabolic enzymes of FH-deficient cells using proteomics and computational modeling, showing PDHA1 inhibition due to increased phosphorylation.

The paper analyzes proteogenomic data to show that chromosomal rearrangements often reduce protein abundance post-transcriptionally, particularly in protein complexes, and identifies regulatory interactions linked to drug resistance.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.

This paper describes a method to identify drug resistance mutations in cancer using genome-wide chemical mutagenesis and next-generation sequencing, enabling the discovery of both known and novel resistance mechanisms.

The paper benchmarks kinase activity inference methods using phosphoproteomic data, finding the GSEA method performs best with an average AUC of 0.722.

This study integrates metabolomics, phosphoproteomics, and transcriptomics data in yeast to identify regulatory mechanisms linking signaling kinases to metabolic changes.

The paper introduces BiRewire 3, a tool for efficiently randomizing biological networks while preserving node functional characteristics, and demonstrates its application in network analysis.

This paper discusses computational methods to predict kinase activity scores from phosphoproteomics data and introduces the Kinase Activity Toolbox.

The paper outlines the logical modelling framework and achievements of the Consortium for Logical Models and Tools, emphasizing model reusability and interoperability.