Nikolaos Meimetis

The paper uses a deep learning framework to analyze drug-induced transcriptomics and identifies a four-node module involved in TGFβ-IFNα competition in MASH.

scLEMBAS models signaling pathway activity at single-cell resolution using a context-aware neural network to predict perturbation responses and capture cell-type-specific interactions.

FlowTransOP translates omics signatures between domains without paired samples or feature mappings, outperforming existing methods when data are sparse or features are weakly correlated.

This study uses a human liver microphysiological system to investigate how insulin, glucose, and free fatty acids contribute to hepatic insulin resistance and its reversibility.

The paper shows that integrating mRNA and protein data improves understanding of metastasis by leveraging complementary and reinforcing signals between modalities.

The paper develops a machine learning framework to identify optimal experimental conditions for improving the translatability of microphysiological systems to in vivo contexts, using MAFLD as a case study.

The paper develops a deep learning model to infer drug-target interactions and their downstream effects on cellular signaling, outperforming basic machine learning approaches and validating predictions with an independent dataset.

The paper develops AutoTransOP, an autoencoder framework to translate omics signatures across species and contexts, outperforming existing methods in identifying predictive molecular features.

The paper develops a recurrent neural network to simulate intracellular signaling by predicting transcription factor activity from ligand concentrations, validated with synthetic and experimental data.

This paper introduces deepSNEM, a graph deep learning model for compound mechanism of action identification using signaling network embeddings from gene expression data.