Author

Thanh Hai Dang

VNU University of Engineering and TechnologyORCID 0000-0002-0666-9958Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Research Focus:protein structure prediction· 2 Organism:bacteria (other)· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Organism:multiple species· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Canh Hao NguyeniD· 1 shared Duc Quang LeiD· 1 shared Huy Kinh Phan· 1 shared Manh Cuong Nguyen· 1 shared Minh Duc CaoiD· 1 shared Nam S. VoiD· 1 shared Quang Thinh TraciD· 1 shared Quynh-Trang Pham Thi· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Canh Hao NguyenORCID

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3 papers

Recent·Most cited

AMRomics: a scalable workflow to analyze large microbial genome collection

Duc Quang Le, Tam Thi Nguyen, Canh Hao Nguyen, Tho Huu Ho, Nam S. Vo, Trang Thu Nguyen, Son Hoang Nguyen, Vinh Sy Le, Thanh Hai Dang, Minh Duc Cao

AMRomics is a scalable pipeline for analyzing large microbial genome collections, showing better performance than existing tools.

BMC Genomics · Jul 22, 2024Peer-reviewedBioinformatics

Sequence-based Protein-Protein Interaction Prediction Using Multi-kernel Deep Convolutional Neural Networks with ProteinLanguage Model

Thanh Hai Dang, Tien Anh Vu

This study introduces xCAPT5, a hybrid model combining CAPT5 and XGBoost for PPI prediction, outperforming existing methods on multiple tasks.

BMC Bioinformatics · Mar 9, 2024Peer-reviewedBioinformatics

SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings

Thanh Hai Dang, Quang Thinh Trac, Huy Kinh Phan, Manh Cuong Nguyen, Quynh-Trang Pham Thi

This paper introduces SKIPHOS, a non-kinase specific phosphorylation site predictor using random forests and amino acid skip-gram embeddings, showing better performance than existing methods for three phosphorylation residues.

bioRxiv · Oct 16, 2019 · PreprintBioinformatics