Rajan Gyawali

TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms

TomoSwin3D improves automated macromolecule detection in 3D cryo-ET tomograms by using a Swin Transformer-based U-Net with enhanced input features and occupancy-preserving supervision.

CryoVirusDB: A Labeled Cryo-EM Image Dataset for AI-Driven Virus Particle Picking

CryoVirusDB provides a labeled cryo-EM dataset for training AI to identify virus particles in micrographs, aiding in 3D structural modeling.

Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination

The paper introduces MICA, a deep learning method that combines cryo-EM density maps and AlphaFold3 predictions to improve protein structure modeling accuracy and completeness.

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-EM micrographs

CryoFSL improves protein particle picking in cryo-EM micrographs with minimal annotations, achieving high accuracy and efficiency in low-SNR conditions.

Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net

The paper introduces CryoSegNet, a method combining a specialized U-Net and SAM for cryo-EM particle picking, achieving higher accuracy than existing methods.

CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs

CryoTransformer improves protein particle picking in cryo-EM micrographs using a transformer model, achieving better resolution and F1-score than existing methods.

CryoPPP: A Large Expert-Labelled Cryo-EM Image Dataset for Machine Learning Protein Particle Picking