Haotian Teng

DTMol is a diffusion model for pocket-based molecular docking that outperforms existing methods on benchmark datasets and identifies new JAK2 inhibitors.

The paper benchmarks poly(A) tail-length estimation tools using synthetic RNA standards, finding all tools accurate within 12% and recommending Dorado for its speed and ease of use.

The paper introduces Xron, a semi-supervised basecaller for detecting m6A methylation from nanopore sequencing by combining synthetic and experimental data.

The paper introduces FICT, a method that improves cell type assignment in spatial transcriptomics data by incorporating both gene expression and neighborhood information, showing better accuracy in identifying cell types and sub-types.

This study compares DNA and RNA nanopore sequencing for detecting drug resistance in XDR K. pneumoniae, finding RNA sequencing identifies more resistance genes and shows strong correlation with qRT-PCR.

Chiron is a deep learning model that directly translates nanopore raw signals into DNA sequences without segmentation, achieving high accuracy and speed with minimal training data.