Author

Mark Craven

University of Wisconsin–Madison

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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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:perturbation response prediction· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Yuriy SverchkoviD· 2 shared Abbye E. McEweniD· 1 shared Alan F. RubiniD· 1 shared Anastasiya Sushkova· 1 shared Anne O’Donnell‐LuriaiD· 1 shared Christopher H. EmfingeriD· 1 shared Daniel ZeibergiD· 1 shared Douglas M. FowleriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

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10 top collaborators · 31 mutual co-author links

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2 shared papersSystems Biology

Yuriy SverchkovORCID

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2 papers

Recent·Most cited

A graph-based learning approach to predict the effects of gene perturbations on molecular phenotypes

Yiyang Jin, Yuriy Sverchkov, Anastasiya Sushkova, Michael Ohtake, Christopher H. Emfinger, Mark Craven

This paper introduces a graph-based machine learning approach to predict gene perturbation effects on molecular phenotypes using existing experimental data and biological networks.

bioRxiv · Mar 25, 2026 · PreprintSystems Biology

Gene-based calibration of high-throughput functional assays for clinical variant classification

Daniel Zeiberg, Ross Stewart, Shantanu Jain, Malvika Tejura, Abbye E. McEwen, Shawn Fayer, Yuriy Sverchkov, Mark Craven, Vikas Rao Pejaver, Alan F. Rubin, Lea M. Starita, Douglas M. Fowler, Anne O’Donnell‐Luria, Predrag Radivojac A

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

bioRxiv · Apr 29, 2025 · PreprintBioinformatics