ArticleBMC bioinformatics2015
Impact of missing data imputation methods on gene expression clustering and classification.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material.Journal of translational medicine · 2025Article
- To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.Journal of the American Society for Mass Spectrometry · 2025Article
- Missing value imputation on gene expression data using bee-based algorithm to improve classification performance.PloS one · 2024Article
- K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data.Advances in drug and alcohol research · 2024Article
- Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data.Frontiers in pediatrics · 2023Review
- Attention-Based Sequence-to-Sequence Model for Time Series Imputation.Entropy (Basel, Switzerland) · 2022Article
- An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Identification of diagnostic markers and immune cell infiltration characteristics in antineutrophil cytoplasmic antibody-associated vasculitis by weighted gene co-expression network analysis.European journal of medical research · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Connecting the dots: The boons and banes of network modeling.Patterns (New York, N.Y.) · 2021Review
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.Journal of integrative bioinformatics · 2020Article
- Clustering of Data with Missing Entries using Non-convex Fusion Penalties.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2019Article
- DeepCC: a novel deep learning-based framework for cancer molecular subtype classification.Oncogenesis · 2019Article
- Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique.International journal of molecular sciences · 2018Article
- Establishment of a SVM classifier to predict recurrence of ovarian cancer.Molecular medicine reports · 2018Article
- DNA-based Determination of Ancestry in Cynomolgus Macaques (Journal of the American Association for Laboratory Animal Science : JAALAS · 2018Article
- CLUSTERING OF DATA WITH MISSING ENTRIES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2018Article
- Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets.Journal of healthcare engineering · 2018Article
- The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.Genomics, proteomics & bioinformatics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. RESULTS AND
conclusionsWe performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/ .
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