Mehrshad Sadria

Deep Lineage uses deep learning to analyze time-series single-cell RNA-sequencing with lineage-tracing data to predict cell fate and gene expression profiles more accurately than existing methods.

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

LineageSim generates single-cell lineage data with fate-aware gene expression to improve cell fate prediction benchmarks.

CLERA identifies gene regulatory programs and dynamic models from single-cell RNA sequencing data using representation learning and sparse model discovery.

FateNet predicts cell fate decisions using scRNA-seq data by integrating dynamical systems theory and deep learning, identifying key genes in hematopoiesis.

This study converts postnatal mouse astrocytes to oligodendrocyte lineage cells using Sox10 or Olig2, supported by single-cell RNA sequencing and computational models.

This paper introduces scVAEDer, a deep-learning model combining variational autoencoders and diffusion models to generate meaningful embeddings for single-cell transcriptomics data, enabling data generation, perturbation prediction, and regulator detection.

This study shows adversarial training improves robustness and interpretability in single-cell RNA-seq cell type classification models.

Fatecode predicts cell fate regulators using scRNA-seq data and deep learning, identifying known master regulators in blood and brain development datasets.

This study develops a computational model to explore the conflicting roles of AMPK in cancer by simulating metabolic signaling pathways and their effects on tumorigenesis.

The paper develops a computational model of ageing signalling pathways to study interactions among mTORC, AMPK, and SIRT, and their impacts on lifespan and health-span.