Miguel Hurtado

This paper develops a multiscale framework linking pharmacokinetics and Boolean models to study ibrutinib resistance in CLL, identifying key regulons and synergistic targets.

CellTFusion identifies functional multicellular states from bulk RNA-seq data by integrating cell type deconvolution and transcriptional regulatory network analysis, revealing TME programs linked to immunotherapy responses.

The paper examines the spatial organization of dendritic cell subsets in NSCLC patients treated with anti-PD1 and finds that cDC1-driven patterns correlate with prolonged ICB response.

The paper introduces pipeML, a framework for leakage-free machine learning in omics data by enabling fold-aware cross-validation, improving model evaluation accuracy.

PhysiGym integrates PhysiCell with Gymnasium to enable reinforcement learning control of in silico biological processes, demonstrating tumor elimination in a case study.

multideconv reduces redundancy and improves accuracy in cell type deconvolution by integrating results from multiple methods and signatures.

The paper uses time-series RNA-Seq and gene regulatory network inference to study how CLL cells respond to immune signals in vitro, revealing dynamic regulatory mechanisms and patient-specific gene modules.