Projects LncRNA expression during cell differentiation Studying cell differentiation has been revolutionized by the advent of single cell technologies. Moving beyond a stepwise view, we now depict a continuum process containing a range of intermediate cell states expressing different transcriptomic programs. Up to now, the annotation and understanding of these intermediate states mostly relied on protein-coding
Publications Deep Learning for automatic signal enhancement in biological imaging Deep Learning for mRNA spot enhancement in fluorescent microscopy FISH images
Projects Exploiting innate immunity of hepatocellular carcinoma to improve patients treatment Analysis of HCC tumour etiology based on multi-omic datasets including scRNAseq, spatial scRNAseq, bulk RNAseq and 16S microbiome sequencing
Projects Antimicrobial resistance prediction by machine learning approach AI method for predicting the minimum inhibitory concentration (MIC) of several antibiotics for antimicrobial resistance (AMR)
Projects Bioinformatics for the study of metabolism in glioblastoma Development of omics data integration methods to elucidate the GBM invasiveness
Projects Unsupervised segmentation of nuclei in fluorescence microscopy images using Deep Learning Development of unsupervised Deep Learning methods for segmentation of liver spheroids acquired with the soSPIM in 3D and prediction of temporal phenotypes of spheroids
Projects Tumoral heterogeneity and multi-omics data integration Tumour heterogeneity analysis by deconvolution and multi-omic data integration to elucidate cancer phenotypes
