Author

J. Dy

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genomics· 1 Pharmacology & Toxicology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Assay:bulk RNA-seq· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

A. Alshawabkeh· 1 shared A. Boueiz· 1 shared A. Iturralde· 1 shared A. Masoomi· 1 shared A. Paul· 1 shared A. Z. Gu· 1 shared B. C. Jones· 1 shared C. P. Hersh· 1 shared

Activity by Year

2020-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperPharmacology & Toxicology

A. Alshawabkeh

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Explainable machine learning reveals an RBP regulatory logic of exon skipping

Y. Raghav, A. Paul, R. Anderson, S. Karthyk, A. Iturralde, J. Vyas, J. Dy, B. C. Jones, P. J. Castaldi, J. Platig

The paper uses explainable machine learning to uncover RBP binding patterns that predict exon skipping and reveals biologically interpretable regulatory logic.

bioRxiv · May 30, 2026 · PreprintSystems Biology

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

S. M. Rahman, J. Lan, D. Kaeli, J. Dy, A. Alshawabkeh, A. Z. Gu

The paper identifies and validates biomarkers using machine learning to predict in vivo phenotypic toxicity endpoints from in vitro toxicogenomic data.

bioRxiv · Dec 18, 2020Pharmacology & Toxicology

Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models

Z. Wang, A. Masoomi, Z. Xu, A. Boueiz, S. Lee, T. Zhao, M. Cho, C. P. Hersh, E. K. Silverman, J. Dy, P. Castaldi

The paper develops deep learning models using RNA-seq data that incorporate isoform and exon information to improve smoking status prediction, achieving higher AUC than gene-level models.

bioRxiv · Sep 9, 2020 · PreprintGenomics