Author

Nolan J. English

Georgia Institute of TechnologyORCID 0000-0002-9841-0731Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:machine learning (classical)· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Organism:human· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

I. King JordaniD· 1 shared Joseph Luper TsenumiD· 1 shared K. Choi· 1 shared Leonardo Mariño‐RamíreziD· 1 shared Manoj S. KambaraiD· 1 shared Matthew P. TorresiD· 1 shared Onyinye Chukka· 1 shared Sonali GuptaiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

I. King JordanORCID

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2 papers

Recent·Most cited

Explainable machine learning for health disparities: type 2 diabetes in the All of Us research program

Manoj S. Kambara, Onyinye Chukka, K. Choi, Joseph Luper Tsenum, Sonali Gupta, Nolan J. English, I. King Jordan, Leonardo Mariño‐Ramírez

This paper uses explainable machine learning to identify and quantify T2D risk factors contributing to health disparities among different racial and ethnic groups in the All of Us research program.

bioRxiv · Feb 18, 2025 · PreprintBioinformatics

SAPH-ire TFx - A Recommendation-based Machine Learning Model Captures a Broad Feature Landscape Underlying Functional Post-Translational Modifications

Nolan J. English, Matthew P. Torres

The paper introduces SAPH-ire TFx, a machine learning model that recommends functional post-translational modifications by analyzing multiple features.

The FASEB Journal · Apr 1, 2020Peer-reviewedBioinformatics