Author

Dajiang Liu

Pennsylvania State UniversityORCID 0000-0002-1591-0635Unclaimed 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· 2 Organism:human· 2 Assay:metabolomics· 2 Assay:bulk RNA-seq· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

Andrew Young KohiD· 2 shared Xiaowei ZhaniD· 2 shared Animesh Anand MishraiD· 1 shared Ann Donnelly· 1 shared Bo Li· 1 shared Christina M. ZarekiD· 1 shared Dajiang J. LiuiD· 1 shared Eric D. AustiniD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBioinformatics

Andrew Young KohORCID

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Author profile →

3 papers

Recent·Most cited

Early Tracheal and Salivary miRNAs in Extremely Preterm Infants Predict BPD-related Pulmonary Hypertension

Tang Li, Shaoyi Zhang, Vincent P. R. Aluquin, Ann Donnelly, Heather Stephens, S T Bahade A S Lanje and S J Sharma S T Bahade A S Lanje and S J Sharma, Steven D. Hicks, Dajiang Liu, Eric D. Austin, Roopa Siddaiah

The paper identifies miRNAs in tracheal and salivary samples of preterm infants that predict BPD-related pulmonary hypertension with improved accuracy when combined with clinical variables.

Pulmonary Circulation · Oct 1, 2026Peer-reviewedBioinformatics

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

Sen Yang, Shidan Wang, Yiqing Wang, Ruichen Rong, Bo Li, Andrew Young Koh, Guanghua Xiao, Dajiang J. Liu, Dajiang Liu, Xiaowei Zhan

This paper introduces a supervised contrastive learning framework for multi-omics prediction models, showing improved accuracy over single-omics approaches in type 2 diabetes and high-fat diet studies.

Frontiers in Microbiomes · Aug 13, 2026Peer-reviewedBioinformatics

Trends in Machine Learning and Feature Selection Stability for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Matched Datasets

Suzette N. Palmer, Animesh Anand Mishra, Christina M. Zarek, Shuheng Gan, Ruheng Wang, Jiwoong Kim, Dajiang Liu, Andrew Young Koh, Xiaowei Zhan

This study evaluates machine learning algorithms and feature selection stability in multi-omics microbiome datasets, finding that nonlinear models and multi-omics integration improve predictive performance and reproducibility.

bioRxiv · Jun 21, 2025 · PreprintBioinformatics