Zhiting Wei

Toward subtask decomposition-based learning and benchmarking for genetic perturbation outcome prediction and beyond

This study introduces STAMP, a novel method for genetic perturbation outcome prediction that improves accuracy across three subtasks and provides a benchmarking framework for evaluating models.

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization

PerturBase is a database that compiles and visualizes single-cell perturbation data from multiple studies, enabling analysis of genetic and chemical perturbations across millions of cells.

Dr. Sim: Similarity Learning for Transcriptional Phenotypic Drug discovery

Dr. Sim is a learning-based framework for inferring transcriptional similarity to improve phenotypic drug discovery, outperforming existing methods on drug annotation and repositioning tasks.

Unexpected CRISPR off-target mutation pattern in vivo are not typicallygermline-like

The paper re-analyzes WGS data from two studies to find that CRISPR off-target mutations include both SNVs and indels, challenging previous conclusions about their rarity.