Maria Brbić

COSMIC, a generative model, quantifies the relationship between nuclear morphology and gene expression using paired single-cell data, revealing morphology-associated genes in prostate cancer and spatial transcriptomics of zebrafish embryos.

The paper introduces immgenT-GP, a gene-program framework that identifies 200 reproducible gene programs in mouse T cells, capturing lineage identity, activation states, and tissue location.

immgenT creates a comprehensive T-cell molecular atlas across mouse organs and disease states, identifying 8 lineages and 107 clusters using deep generative models and integrating external datasets.

MORPHE synthesizes biologically faithful tissue architecture from spatial-omics data, enabling reconstruction and extension of tissue context beyond experimental limitations.

The paper introduces IRIS, a platform integrating high-resolution imaging and single-cell RNA sequencing to link morphology with transcriptomes, revealing new nuclear-ER architectures in CD8+ T cells.

ACE identifies universal aging signatures across species by disentangling aging-related gene expression from other biological variations using explainable AI.

The paper identifies TCR-V gene usage patterns in T cells sorted by anti-TCR mAbs using single-cell RNAseq and finds enriched ERV1-family LTR expression in specific TRBV regions.

The paper examines T cell changes in mammary glands during pregnancy and lactation, finding an expansion of T effector cells and similarities to intestinal T cells.

Trained on over 1.3 million bacterial genomes represented as ordered protein sequences, the transformer foundation model Bacformer predicts protein interactions, operons, essentiality, and phenotypes while generating trait-conditioned synthetic proteomes.

LUNA reconstructs tissue architecture from dissociated single-cell RNA-seq data using spatial priors learned from spatial transcriptomics, validating on mouse brain datasets and disease models.

SATURN integrates single-cell RNA-seq datasets across species using deep learning to create universal cell embeddings, enabling cross-species analysis and annotation transfer.

This study introduces GeneBac, a deep learning model that predicts antibiotic resistance from bacterial genomic sequences, showing accurate MIC prediction and variant effect assessment.

The paper presents an aging Drosophila cell atlas, identifies cell type-specific aging patterns, and shows ribosomal gene expression as a conserved aging predictor.

STELLAR annotates spatially resolved single-cell data using geometric deep learning, discovering cell types and states while transferring annotations across tissues and donors.

The paper maps chemosensory co-receptor expression in Drosophila olfactory neurons, revealing widespread co-expression patterns among Orco, Ir25a, Ir8a, and Ir76b.

The paper creates a single-cell transcriptomic atlas of the adult fruit fly, identifying over 250 cell types and analyzing gene signatures and sexual dimorphism across tissues.

The paper maps cell organization and function across human intestine regions using single nuclear RNA and open chromatin assays, revealing distinct cell neighborhoods and regulatory differences.

The paper generates single-cell transcriptomes of Drosophila olfactory receptor neurons across development and adulthood, revealing stable receptor expression and cell-type-specific transcriptomic features.

The paper analyzes single-cell transcriptomes of Drosophila olfactory projection neurons across developmental stages to identify transcriptomic changes and cellular diversity patterns.

MARS identifies and annotates known and novel cell types in single-cell RNA-seq data by transferring latent representations across datasets and generating interpretable names for new cell types.

OnClass classifies single-cell data into Cell Ontology terms and identifies marker genes for all categories, even those absent in training data.