Author

Connor Meehan

ORCID 0000-0002-7596-2437Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:clustering· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:matrix factorization· 1

Frequent Co-Authors

Guenther WaltheriD· 3 shared David R. ParksiD· 2 shared Leonore A. HerzenbergiD· 2 shared Stephen Meehan· 2 shared Wayne MooreiD· 2 shared Andrei V. ChernysheviD· 1 shared C. SchilleriD· 1 shared Claire M. Simpson· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Guenther WaltherORCID

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3 papers

Recent·Most cited

Automatic Phenotyping Using Exhaustive Projection Pursuit

Wayne Moore, Stephen Meehan, Connor Meehan, David R. Parks, Guenther Walther, Leonore A. Herzenberg

This paper introduces EPP, a method for automated phenotype identification in flow cytometry data, and evaluates it on four datasets.

Communications Biology · Aug 12, 2025Peer-reviewedBioinformatics

MUDflow: Combining Neural Networks, UMAP and DBM Clustering to Identify Cell Populations Accurately, Quickly and Easily in Mass and Fluorescence Cytometry

Connor Meehan, Stephen Meehan, Jonathan Ebrahimian, Wayne Moore, David R. Parks, Guenther Walther, Leonore A. Herzenberg

The paper introduces MLPgater, a neural network for automatic flow cytometry gating, and MUDflow, which combines it with UMAP and DBM clustering to improve population detection accuracy and speed.

bioRxiv · Jun 19, 2025 · PreprintBioinformatics

Lifting the curse from high dimensional data: Automated projection pursuit clustering for the variety of biological data modalities

Claire M. Simpson, Evgeniy Tabatsky, Zainab Rahil, Devon James Eddins, Sasha L. Tkachev, Florian Georgescauld, Derek Papalegis, Martin Culka, Tyler M. Levy, Ivan V. Gregoretti, Connor Meehan, C. Schiller, Kresimir Bestak, Denis Schapiro, Andrei V. Chernyshev, , Darya Yu. Orlova

The paper introduces APP, a clustering method that reduces high-dimensional biological data to lower dimensions to improve clustering performance across multiple data types.

GigaScience · Jan 1, 2025Peer-reviewedBioinformatics