#6023. Evaluation of different approaches for missing data imputation on features associated to genomic data

July 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computational Mathematics;
Computational Theory and Mathematics;
Computer Science Applications;
Genetics;
Biochemistry;
Molecular Biology;
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Abstract:
Background: Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis. The data collection process can lead to different distribution, frequency, and structure of missing data points. They can be classified into four categories: Structurally Missing Data (SMD), Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR). For the three later, and in the context of genomic data (especially non-coding data), we will discuss six imputation approaches using 31,245 variants collected from ClinVar and annotated with 13 genome-wide features.
Keywords:
genomics; imputation; Machine learning; missing data; pathogenic variants

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