Heba Sailem

The paper uses multiplexed imaging and graph convolutional networks to analyze mTOR signaling heterogeneity in colorectal tumors and its correlation with patient prognosis.

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.