Renmin Han

CryoMV generates and validates continuous protein conformations from cryo-EM data using structure priors and real-particle validation, achieving high resolution and robustness on benchmark datasets.

CryoForge is an evidence-gated agent that improves cryo-EM model building by selectively accepting or rejecting edits based on structural evidence, achieving validated improvements in most test cases.

WattmaMod detects multiple RNA modifications from nanopore sequencing data using deep learning, achieving high accuracy and extending to low-resource modifications.

CryoDiff enhances cryo-EM maps using a diffusion model with uncertainty estimation, improving resolution and model completeness.

centroAnno is a de novo algorithm for centromere annotation that outperforms existing tools in accuracy and efficiency on simulated and real datasets.

PGAR-Zernike provides a fast and accurate open-source toolkit for computing Zernike descriptors of protein structures, enabling efficient structural database construction and retrieval.

This paper introduces trRosettaRNA, a deep learning method for RNA 3D structure prediction, showing improved accuracy over existing methods on benchmark datasets.

This paper introduces a hybrid unsupervised method for accurate short read clustering and barcoded sample demultiplexing in nanopore sequencing, outperforming traditional tools in clustering and matching learning-based methods in demultiplexing.

Noise-Transfer2Clean improves cryo-EM image denoising by modeling and transferring noise characteristics, achieving better signal restoration than existing methods.

This paper introduces two novel algorithms for efficiently searching and mapping genomic subsequences in nanopore raw signals, improving SNP detection and haplotype classification under low coverage and accuracy.

PureseqTM predicts transmembrane topology from amino acid sequences, outperforming existing methods and correcting UniProt annotations in the human proteome.

The paper introduces cwDTW, a wavelet-based DTW algorithm for nanopore sequencing signal alignment, achieving high accuracy and speed compared to existing methods.

DeepSimulator generates realistic Nanopore sequencing data using a context-dependent deep learning model, improving signal accuracy and enabling tool development.