Weizhong Zheng

Generative inverse design steers human hematopoietic cell fate in vitro

This paper introduces LabCompass, a generative inverse-modeling framework that optimizes culture conditions to steer human hematopoietic cell fate in vitro, demonstrating its effectiveness through iterative experimental validation.

Pseudodynamics+: Reconstructing Population Dynamics from Time-Resolved Single Cell Landscapes with Physics Informed Neural Networks

Pseudodynamics+ reconstructs population dynamics from time-resolved single-cell data using physics-informed neural networks, revealing dynamic changes in cell state parameters.

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

CellHermes integrates multimodal omics data using a biological language model, achieving comparable or better performance than single-cell models across various tasks.

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

This paper introduces inDecay, a few-shot learning system for predicting CRISPR editing outcomes in cell lines and zygotes, achieving high accuracy with minimal data.

Multi-task learning uncovers robust translation cis-regulatory features

This paper develops MTtrans, a multi-task learning model that identifies robust, generalizable cis-regulatory motifs in 5' UTRs by integrating diverse datasets, showing improved accuracy and cross-species transferability.

Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis

The paper defines the single-cell spatiotemporal gene expression landscape of human embryonic development using stem-cell-derived organoids and identifies key developmental signatures and signaling pathways.