March 3, 2026Open Access

A systematic assessment of machine learning for structural variant filtering

Key Points

Optimal germline structural variant filtering involves simpler models, which ensure accuracy and speed.
The analysis identifies a clear benchmark: transparent models outperform complex alternatives in predicting structural variants.
Evaluation focuses on machine learning techniques within germline SV contexts, promoting effective method selection.
Implications highlight the necessity of justifying model complexity with unmet biological needs rather than minor performance improvements.

Abstract

Our findings demonstrate that for the established task of germline SV filtering, simpler, interpretable models provide an optimal balance of accuracy, speed, and transparency. This benchmark establishes a pragmatic framework for method selection and argues that increased model complexity must be justified by clear, unmet biological needs rather than marginal predictive gains.

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Authors

Archit Kalra

Luis Paulin

FRITZ SEDLAZECK

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Institutions

Baylor College of Medicine

Rice University

Kennedy Center

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

A systematic assessment of machine learning for structural variant filtering

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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