ArticleNucleic acids research2006
Microarray missing data imputation based on a set theoretic framework and biological knowledge.
Article in Nucleic acids research, 2006. 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.
- 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
- ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.Briefings in bioinformatics · 2020Article
- Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms.BMJ open diabetes research & care · 2020Article
- Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification.Frontiers in pharmacology · 2018Article
- Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.Frontiers in physiology · 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
- A glance at the applications of Singular Spectrum Analysis in gene expression data.Biomolecular detection and quantification · 2015Review
- A hybrid imputation approach for microarray missing value estimation.BMC genomics · 2015Article
- Missing value imputation for microarray data: a comprehensive comparison study and a web tool.BMC systems biology · 2013Article
- Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.Theoretical biology & medical modelling · 2011Article
- A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data.Proteome science · 2010Article
- Multiconstrained gene clustering based on generalized projections.BMC bioinformatics · 2010Article
- Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments.BMC genomics · 2010Article
- How to improve postgenomic knowledge discovery using imputation.EURASIP journal on bioinformatics & systems biology · 2009Article
- Discovering biclusters in gene expression data based on high-dimensional linear geometries.BMC bioinformatics · 2008Article
- Missing value imputation improves clustering and interpretation of gene expression microarray data.BMC bioinformatics · 2008Article
- Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.BMC bioinformatics · 2008Article
- The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.Nature reviews. Cancer · 2008Review
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3 authors.
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Abstract
Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.
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