Author
Nabin Giri
University of MissouriORCID 0000-0002-0251-3956Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2023-2025 · peak 2/yrCo-Author Collaboration Network
6 top collaborators · 9 mutual co-author links6 papers
Recent·Most cited
- BioinformaticsComputational and Structural Biotechnology JournalPeer-reviewed
A Labeled Dataset for AI-based Cryo-EM Map Enhancement
Nabin Giri, Xiao Chen, Liguo Wang, Jianlin Jack Cheng
The paper introduces a labeled dataset for AI-based cryo-EM map denoising, including experimental maps and three types of generated label maps for benchmarking.
2 citations - BioinformaticsNature CommunicationsPeer-reviewed
De Novo Atomic Protein Structure Modeling for Cryo-EMDensity Maps Using 3D Transformer and Hidden MarkovModel
Nabin Giri, Jianlin Jack Cheng
Cryo2Struct improves de novo atomic protein structure modeling from cryo-EM density maps using a 3D transformer and HMM, outperforming Phenix on multiple metrics.
47 citationstop 1% - BioinformaticsScientific DataPeer-reviewed
Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures
Nabin Giri, Liguo Wang, Jianlin Jack Cheng
The paper introduces Cryo2StructData, a large labeled cryo-EM density map dataset for training AI models to build atomic protein structures, showing it is larger and higher quality than existing datasets.
- BioinformaticsbioRxivPreprint
Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15
Jianlin Jack Cheng, Raj S. Roy, Jian Liu…·Zhiye Guo
The paper introduces a hybrid method combining pairwise similarity and deep learning for estimating protein complex model accuracy, achieving high correlation and low ranking loss in CASP15.
- BioinformaticsBiomoleculesPeer-reviewed
Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge
Nabin Giri, Jianlin Jack Cheng
This paper introduces DeepProLigand, a deep learning pipeline that improves protein-ligand interaction modeling using cryo-EM data and templates, achieving top performance in a benchmark challenge.