Author

Haeun Moon

Seoul National UniversityORCID 0009-0000-4792-9069Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:imputation· 1 Assay:proteomics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Kathryn RoederiD· 2 shared Aaron K. JenkinsiD· 1 shared Anna L. MarslandiD· 1 shared Antoine B. Douaihy· 1 shared Bernie DevliniD· 1 shared Colleen A. McClungiD· 1 shared David A. BrentiD· 1 shared David A. LewisiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Kathryn RoederORCID

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3 papers

Recent·Most cited

A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism

Haeun Moon, Laura Gail Sloofman, Marina Natividad Avila, Lambertus Klei, Bernie Devlin, Joseph D. Buxbaum, Kathryn Roeder

The paper introduces a framework to infer de novo exonic variants without parental genotypes, enhancing autism association studies by improving gene-based tests and controlling false discovery rates.

Bioinformatics · Apr 28, 2026Peer-reviewedBioinformatics

Unraveling Tissue-Specific Molecular Signatures and Convergent Pathway Enrichments in Suicidal Behavior

Aaron K. Jenkins, Meilin Jia-Richards, Madeline R. Scott, Eli Goodfriend, RuoFei Yin, Sarah Riston, Kyle D. Ketchesin, Haeun Moon, Kaitlyn A. Petersen, Antoine B. Douaihy, Jill R. Glausier, David A. Brent, David A. Lewis, Anna L. Marsland, George C. Tseng, , Nadine Melhem

This study identifies tissue-specific molecular signatures and convergent pathways in suicide by analyzing transcriptomic data from blood and brain tissues.

bioRxiv · Feb 27, 2026 · PreprintNeuroscience

Augmented Doubly Robust Post-Imputation Inference for Proteomic data

Haeun Moon, Jin‐Hong Du, Jing Lei, Kathryn Roeder

The paper introduces a statistical framework for proteomic data analysis that combines imputation with debiasing to improve inference and reduce false positives in both single-cell and bulk data.

The Annals of Applied Statistics · May 28, 2025Peer-reviewedBioinformatics