ArticleIEEE/ACM transactions on computational biology and bioinformatics
Incorporating Nonlinear Relationships in Microarray Missing Value Imputation.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Nonlinear embedding and integration of omics data: a fast and tuning-free approach.Briefings in bioinformatics · 2025Article
- Genomic data imputation with variational auto-encoders.GigaScience · 2020Article
- Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach.Statistical analysis and data mining · 2018Article
- Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.Bioinformatics (Oxford, England) · 2018Article
- Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.Journal of biomedical informatics · 2015Article
- K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.BioMed research international · 2015Article
- Exploiting identifiability and intergene correlation for improved detection of differential expression.ISRN bioinformatics · 2013Article
- Shrinkage regression-based methods for microarray missing value imputation.BMC systems biology · 2013Article
- Nonlinear dependence in the discovery of differentially expressed genes.ISRN bioinformatics · 2012Article
- Hierarchical clustering of high-throughput expression data based on general dependences.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
3 authors.
Funding
Abstract
Microarray gene expression data often contain missing values. Accurate estimation of the missing values is important for downstream data analyses that require complete data. Nonlinear relationships between gene expression levels have not been well-utilized in missing value imputation. We propose an imputation scheme based on nonlinear dependencies between genes. By simulations based on real microarray data, we show that incorporating nonlinear relationships could improve the accuracy of missing value imputation, both in terms of normalized root-mean-squared error and in terms of the preservation of the list of significant genes in statistical testing. In addition, we studied the impact of artificial dependencies introduced by data normalization on the simulation results. Our results suggest that methods relying on global correlation structures may yield overly optimistic simulation results when the data have been subjected to row (gene)-wise mean removal.
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