Bárbara Hernando

This paper uses machine learning to predict genomic alterations from H&E images of liver tumors, revealing genotype-phenotype links and highlighting genetic background limitations.

This paper identifies a chromosomal instability (CIN) biomarker that predicts response to PORCN inhibitors in cancers, validated through in vitro models, cell lines, and patient data.

This paper develops a computational framework using single-cell whole-genome sequencing to identify ongoing chromosomal instability, showing its utility in cancer models and clinical samples.

This paper develops a framework to predict future oncogene amplification or tumour suppressor deletion in cancer using genomic data, achieving an average AUC of 0.87.

CNpare matches cancer cell lines based on high-resolution DNA copy number profiles, accurately identifying matched lines in all tested cases.