Our latest science, research and ideas

Joint probabilistic modeling of pseudobulk and single-cell transcriptomics enables accurate estimation of cell type composition

Robust sensitivity control in digital pathology via tile score distribution matching

Predicting gene essentiality and drug response from perturbation screens in preclinical cancer models with LEAP: Layered Ensemble of Autoencoders and Predictors

Distilling foundation models for robust and efficient models in digital pathology

AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Legitimate ground-truth-free metrics for deep uncertainty classification scoring

Phikon-v2, A large and public feature extractor for biomarker prediction

MYC Rearrangement Prediction from LYSA Whole Slide Images in Large B-cell Lymphoma: A Multi-centric Validation of Self-supervised Deep Learning Models

CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images