A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Comparison of missing data handling methods for variant pathogenicity predictors
Tekijät: Särkkä, Mikko; Myöhänen, Sami; Marinov, Kaloyan; Saarinen, Inka; Lahti, Leo; Fortino, Vittorio; Paananen, Jussi
Kustantaja: Oxford University Press (OUP)
Julkaisuvuosi: 2025
Lehti:: NAR Genomics and Bioinformatics: Nucleic Acids Research Genomics and Bioinformatics
Artikkelin numero: lqaf133
Vuosikerta: 7
Numero: 4
ISSN: 2631-9268
eISSN: 2631-9268
DOI: https://doi.org/10.1093/nargab/lqaf133
Verkko-osoite: https://doi.org/10.1093/nargab/lqaf133
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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No external funding.
