ArticleBioData mining2021
Evaluation of different approaches for missing data imputation on features associated to genomic data.
Article in BioData mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 50 citations in OpenAlex.
- A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.BMC medical research methodology · 2026Article
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- Differences in plasma metabolome between non-Hispanic White and non-Hispanic Black women.BMC medicine · 2025Article
- Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.Sensors (Basel, Switzerland) · 2025Article
- Changes in social relationships from 26 to 34 years of age in adults born very preterm.Paediatric and perinatal epidemiology · 2025Article
- Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.Cell reports methods · 2024Article
- PFAS Levels, Early Life Factors, and Mammographic Breast Density in Premenopausal Women.Environmental health perspectives · 2024Article
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods.Scientific reports · 2024Article
- Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.Clinical and molecular hepatology · 2024Article
- Exploring the use of Artificial Genomes for Genome-wide Association Studies through the lens of Utility and Privacy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics · 2023Article
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- Artificial neural network applied to fragile X-associated tremor/ataxia syndrome stage diagnosis based on peripheral mitochondrial bioenergetics and brain imaging outcomes.Scientific reports · 2022Article
- Highlights of the 1st Ecuadorian-Venezuelan Symposium of Young Researchers in Bioinformatics (1SEVJIB).F1000Research · 2022Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMissing 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.
resultsRandom Forest and kNN algorithms showed the best performance in the evaluated dataset. Additionally, some features show robust imputation regardless of the algorithm (e.g. conservation scores phyloP7 and phyloP20), while other features show poor imputation across algorithms (e.g. PhasCons). We also developed an R package that helps to test which imputation method is the best for a particular data set.
conclusionsWe found that Random Forest and kNN are the best imputation method for genomics data, including non-coding variants. Since Random Forest is computationally more challenging, kNN remains a more realistic approach. Future work on variant prioritization thru genomic screening tests could largely profit from this methodology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.