Author

Duangdao Wichadakul

Chulalongkorn UniversityORCID 0000-0002-8802-2524Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:deep learning· 2 Organism:human· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

Amarin JettakuliD· 1 shared Hasnee Noh· 1 shared Khuanwara - Potiwara· 1 shared Kornkiat VongpaisarnsiniD· 1 shared Nuttachai Kulthammanit· 1 shared Peerapon VateekuliD· 1 shared Poonyapat SukawutthiyaiD· 1 shared Rattaphon LiniD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 7 mutual co-author links

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1 shared paperBioinformatics

Amarin JettakulORCID

Mutual co-authors in cluster

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4 papers

Recent·Most cited

CoCoBin: Graph-Based Metagenomic Binning via Composition-Coverage Separation

Khuanwara - Potiwara, Duangdao Wichadakul

CoCoBin improves metagenomic binning by separating composition and coverage features to cluster contigs into microbial genomes, outperforming existing tools in bin count and F1 score.

bioRxiv · Aug 27, 2025 · PreprintBioinformatics

STRategy: A support system for collecting and analyzing short tandem repeats for forensic science

Nuttachai Kulthammanit, Poonyapat Sukawutthiya, Hasnee Noh, Kornkiat Vongpaisarnsin, Duangdao Wichadakul

STRategy is a web-based platform for managing and analyzing STR data from NGS, offering visualization and role-based access control.

bioRxiv · Feb 20, 2023 · PreprintGenetics

Interpretable deep learning model reveals subsequences of various functions for long non-coding RNA identification

Rattaphon Lin, Duangdao Wichadakul

Xlnc1DCNN is a deep learning model that identifies long non-coding RNAs by analyzing sequence features and provides explanations for its predictions.

bioRxiv · Feb 11, 2022 · PreprintBioinformatics

Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations

Amarin Jettakul, Duangdao Wichadakul, Peerapon Vateekul

The paper presents a deep learning model for biomedical relation extraction between bacteria and biotopes, achieving higher F1 scores than existing methods.

BMC Bioinformatics · Dec 1, 2019Peer-reviewedBioinformatics