Amir Ali Moinfar

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

This paper introduces MapQC, a method to evaluate the quality of mapping query datasets to reference atlases, showing it improves the detection of disease-specific cell states.

DRVI is a deep generative model that learns interpretable biological factors from single-cell omics data without supervision, achieving both disentanglement and integration performance.

The paper introduces MultiMIL, a deep learning framework for phenotype prediction and cell state identification using single-cell data from COVID-19 patients and other datasets.

This paper improves scRNA-seq dataset integration by introducing regularization strategies for cVAEs, enhancing batch correction while preserving biological variation.

This paper introduces scPoli, a deep learning model for integrating single-cell datasets, enabling multi-scale analysis of population-level data by linking sample and cell-level features.