ArticleBMC bioinformatics2008
Missing value imputation for microarray gene expression data using histone acetylation information.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- CLCluster: A redundancy-reduction contrastive learning-based clustering method of cancer subtype based on multi-omics data.Molecular therapy. Nucleic acids · 2025Article
- i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability.Computational and structural biotechnology journal · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour.Scientific reports · 2021Article
- Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.Korean journal of radiology · 2021Article
- An efficient ensemble method for missing value imputation in microarray gene expression data.BMC bioinformatics · 2021Article
- Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.Frontiers in cell and developmental biology · 2021Article
- NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses.Nucleic acids research · 2020Article
- Ahypoxia-related signature enhances the prediction of the prognosis in hepatocellular carcinoma patients and correlates with sorafenib treatment response.American journal of translational research · 2020Article
- Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways.Frontiers in oncology · 2019Article
- A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.Briefings in bioinformatics · 2018Article
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.BMC bioinformatics · 2017Article
- An integrative imputation method based on multi-omics datasets.BMC bioinformatics · 2016Article
- Missing value imputation for microRNA expression data by using a GO-based similarity measure.BMC bioinformatics · 2016Article
- Missing value imputation for microarray data: a comprehensive comparison study and a web tool.BMC systems biology · 2013Article
- Incorporating Nonlinear Relationships in Microarray Missing Value Imputation.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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7 authors.
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Abstract
backgroundIt is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.
resultsThe paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.
conclusionWe demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.
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