Alberto Valdeolivas

This study maps the spatiotemporal organization of minimal residual disease in BRCA1-deficient mouse and human breast tumors, identifying a drug-tolerant EMT-like cell population linked to poor clinical outcomes.

This study uses spatial transcriptomics and single-nucleus RNA sequencing to analyze rAAV2 and rAAV9 vector effects in mouse livers, revealing sex-specific transcriptomic changes and novel transgene co-expression factors.

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

X-Pression reconstructs 3D expression signatures from volumetric microcomputed tomography data, demonstrating its utility in analyzing vaccine efficacy and viral replication in mouse lungs.

Chrysalis identifies tissue compartments in spatial transcriptomics using archetypal analysis and spatially variable gene detection without external references.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

This paper introduces a geometric deep learning model for context-aware protein representations, improving therapeutic target nomination in immune-related diseases.

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

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

The paper identifies plasma membrane cholesterol depletion as a key mechanism of antiviral drug activity against SARS-CoV-2 through gene expression analysis and functional experiments.

This study uses single-cell RNA-seq data to analyze how SARS-CoV-2 infection alters intestinal epithelial-immune cell interactions and identifies potential drug targets and repurposing strategies for COVID-19.

The paper integrates intra- and intercellular signaling knowledge into a database for multicellular omics analysis, demonstrating its application to SARS-CoV-2 infection and ulcerative colitis.

This study profiles the proteomes and phosphoproteomes of four prostate cell lines to identify differentially expressed proteins and phosphosites associated with resistance and cancer progression.

The paper extends the random walk with restart algorithm to multiplex and heterogeneous biological networks to improve disease gene prediction.