Jinyan Li

STUltra integrates spatial transcriptomics data to map disease traits onto spatiotemporal tissue landscapes, outperforming existing methods in scalability and speed.

The study examines EM fungal communities associated with Pinus tabuliformis, finding deterministic processes dominate community assembly over stochastic processes.

This paper surveys PCR-deduplication and error-correction methods on short reads, finding that computational approaches differ significantly from UMI-based methods and leave many errors uncorrected.

The paper identifies optimal workflow combinations for proteomics differential expression analysis using frequent pattern mining and ensemble inference, improving accuracy across multiple quantification platforms.

This paper introduces a method to correct millions of erroneous short reads by leveraging graph learning on edit distances, improving downstream applications like genome abundance quantification and SNP profiling.

This study shows that m6A modification regulates the transition from retinal progenitor cells to glial cells, affecting retinal development and function.

The paper introduces a multi-task deep learning method for predicting protein structural properties and shows it achieves similar or better performance than existing methods.