Author

William F. Kindschuh

Columbia University Irving Medical CenterORCID 0000-0002-7433-7808Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Microbiology· 2 Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:metagenomics· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Assay:metabolomics· 1

Frequent Co-Authors

Tal KoremiD· 3 shared Anne‐Catrin UhlemanniD· 2 shared Aya Brown KaviD· 2 shared Erin WattersiD· 2 shared Heekuk ParkiD· 2 shared Julia A. UrbaniD· 2 shared Andrea Nicole LambertiD· 1 shared Brian M. Mercer· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Tal KoremORCID

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Author profile →

4 papers

Recent·Most cited

Metagenomics reveals a phylogenetically informed microbial signature associated with Morgellons disease

Andrea Nicole Lambert, William F. Kindschuh

The paper uses metagenomic analysis to identify a microbial signature in Morgellons disease lesions, suggesting potential microbial involvement.

bioRxiv · Apr 16, 2026 · PreprintMicrobiology

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles

Julia Urban, Aya Brown Kav, William F. Kindschuh, Heekuk Park, Raiyan Rashid Khan, Erin Watters, Itsik Pe’er, Anne‐Catrin Uhlemann, Tal Korem

The paper identifies sample processing errors in microbiome studies using host genetic profiles from metagenomic sequencing and experimental metadata.

bioRxiv · Sep 7, 2025 · PreprintBioinformatics

Early prediction of preeclampsia using the first trimester vaginal microbiome

William F. Kindschuh, George I. Austin, Yoli Meydan, Heekuk Park, Julia A. Urban, Erin Watters, Susan V. Pollak, George Robert Saade, Judith M. Chung, Brian M. Mercer, William A. Grobman, David M. Haas, Robert M. Silver, Myrna G. Serrano, Gregory A. Buck, , Tal Korem

This study predicts preeclampsia using first trimester vaginal microbiome data with machine learning, achieving an auROC of 0.78 in the training set and 0.80 in an independent cohort.

bioRxiv · Dec 1, 2024 · PreprintMicrobiology

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

William F. Kindschuh, Federico Baldini, Martin C. Liu, Kristin D. Gerson, Jingqiu Liao, Harry H. Lee, Lauren Anton, Pawel Gajer, Jacques Ravel, Maayan Levy, Michal A. Elovitz, Tal Korem

This study identifies vaginal metabolites associated with spontaneous preterm birth and shows they can predict risk with high accuracy using machine learning.

Nature Microbiology · Jan 12, 2023Peer-reviewedSystems Biology