Xian Zhang

DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness

DELPHAI predicts heterogeneous perturbation responses by jointly training a fitness network and optimal transport network to identify differentially expressed genes and depleted cell lineages.

DELPHAI, AI Agent for Predicting Drug Response and Resistance

DELPHAI predicts drug response and resistance using single-cell perturbation data from organoids, identifying mechanisms and recommending therapies.

High-content cellular screen image analysis benchmark study

This study benchmarks deep learning and feature engineering methods for analyzing high-content microscopy images to predict phenotypes and cluster cells without manual annotations.

Fully unsupervised deep mode of action learning for phenotyping high-content cellular images

This paper introduces a fully unsupervised deep learning method to cluster cellular images by Mode-of-Action using only pixel intensity values, achieving high accuracy on a labeled dataset.

Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

This study implements Bayesian neural networks to improve classification accuracy and analyze uncertainty in biomedical imaging datasets.

Unsupervised phenotypic analysis of cellular images with multi-scale convolutional neural networks

This paper develops an unsupervised deep learning method for analyzing cellular images to identify phenotypes and compound effects without segmentation or manual annotations.