Heba Sailem

The paper introduces an Adaptive Multi-Scale Graph Transformer (AMGT) for histopathological image classification, achieving better performance and efficiency than existing models.

The paper compares endogenous small RNAs with synthetic siRNAs using RNA-FM and biological features, finding piRNAs have higher GC content and melting temperature than miRNAs and siRNAs.

HRDPath predicts HRD status from HGSOC histopathology images using a multi-model deep learning architecture, achieving higher AUC and specificity than prior methods.

TissuePlot is a visualization tool for spatial data that enables multi-scale analysis of molecular, cellular, and tissue-level information in spatial context.

The paper introduces a minibatch Bayesian inference method to efficiently analyze high-throughput imaging data, revealing density-dependent interactions in wound healing.

ShapoGraphy provides an interactive visualisation tool for creating pictorial representations of bioimaging data to enhance understanding and analysis of complex phenotypic information.

The paper develops a high-content analysis framework to quantify endothelial network morphology and identifies glutamate receptor-targeting drugs that enhance branching and connectivity in angiogenesis.

KDML predicts multi-scale gene functions from genetic perturbation screens, outperforming traditional methods and identifying clinically relevant gene function associations in colorectal cancer.