CTGLab

Our lab

CTGLab develops innovative computational solutions and analytical frameworks to transform multi-omics and clinical data into actionable insights for Precision Medicine. Our multidisciplinary approach integrates mathematics, statistics, machine learning, and computer science to decode the complexity of cancer biology and advance personalized therapeutic strategies research activities focus on the development of computational solutions and models to transform molecular and clinical experimental data into relevant information for clinical decision-making in the field of Precision Medicine. The approach, as the team, is multidisciplinary combining mathematics, statistics and computer science to dissect the complexity of biological and medical data.

Main research areas

Structural variant detection and characterization

Development of algorithms for identifying germline and somatic genomic alterations from next-generation and long-read sequencing data, including copy number variants, structural rearrangements, and clonality analysis

Gene regulatory network inference

Investigating enhancer-promoter interactions, transcription factor networks, and chromatin accessibility landscapes that govern cell state transitions in cancer and neurodevelopmental disorders

Tumor heterogeneity and spatial genomics

Integrating single-cell and spatial multi-omics profiling to dissect patient-specific cellular diversity, microenvironmental interactions, and therapy resistance mechanisms

Emerging technologies for precision oncology

Pioneering applications of Oxford Nanopore long-read sequencing for real-time genomic and epigenomic profiling, and exploring quantum machine learning approaches for high-dimensional omics data analysis

Translational biomarker discovery

Identifying non-invasive molecular signatures from liquid biopsy and tissue samples for cancer diagnosis, therapeutic monitoring, patient stratification, and treatment response prediction

Multi-omics data integration

Creating computational pipelines that combine genomic, transcriptomic, epigenomic, and medical imaging data with clinical information to enable personalized cancer care