Author

Ines I. Atmosukarto

Kementerian Kesehatan Republik IndonesiaORCID 0000-0002-8802-3893Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

C X LiuiD· 1 shared H Y Wang· 1 shared Haoyue ZhuiD· 1 shared Io Hong CheongiD· 1 shared Nguyen Quoc DatiD· 1 shared Nguyen Trung Thanh· 1 shared Pengfei Zhou· 1 shared Wiku Adisasmito· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

C X LiuORCID

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1 paper

Recent·Most cited

Benchmarking the translational potential of AI-based drug-resistance prediction from Mycobacterium tuberculosis whole-genome sequencing data

C X Liu, Haoyue Zhu, Pengfei Zhou, Nguyen Trung Thanh, Nguyen Quoc Dat, Ines I. Atmosukarto, Io Hong Cheong, Zisis Kozlakidis, Wiku Adisasmito, Xiaoqi Zheng, H Y Wang, Yang Yang

This paper benchmarks AI-based drug-resistance prediction models for Mycobacterium tuberculosis using a large unified dataset, finding XGBoost performs best in precision-recall and F1 metrics, while neural models excel in AUROC.

bioRxiv · Jul 3, 2026 · PreprintBioinformatics