A1 Refereed original research article in a scientific journal
Comparison of missing data handling methods for variant pathogenicity predictors
Authors: Särkkä, Mikko; Myöhänen, Sami; Marinov, Kaloyan; Saarinen, Inka; Lahti, Leo; Fortino, Vittorio; Paananen, Jussi
Publisher: Oxford University Press (OUP)
Publication year: 2025
Journal:: NAR Genomics and Bioinformatics: Nucleic Acids Research Genomics and Bioinformatics
Article number: lqaf133
Volume: 7
Issue: 4
ISSN: 2631-9268
eISSN: 2631-9268
DOI: https://doi.org/10.1093/nargab/lqaf133
Web address : https://doi.org/10.1093/nargab/lqaf133
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/504736398
Modern clinical genetic tests utilize next-generation sequencing (NGS) approaches to comprehensively analyze genetic variants from patients. Out of millions of variants, clinically relevant variants that match the patient's phenotype must be identified accurately and rapidly. As manual evaluation is not a feasible option for meeting the speed and volume requirements of clinical genetic testing, automated solutions are needed. Various machine learning (ML), artificial intelligence (AI), and in silico variant pathogenicity predictors have been developed to solve this challenge. These solutions rely on comprehensive data and struggle with the sparse genetic annotations. Therefore, careful treatment of missing data is necessary, and the selected methods may have a huge impact on the accuracy, reliability, speed and associated computational costs. We present an open-source framework called AMISS that can be used to evaluate performance of different methods for handling missing genetic variant data in the context of variant pathogenicity prediction. Using AMISS, we evaluated 14 methods for handling missing values. The performance of these methods varied substantially in terms of precision, computational costs, and other attributes. Overall, simpler imputation methods and specifically mean imputation performed best.
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Funding information in the publication:
No external funding.
