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Impact of missing data imputation methods on gene expression clustering and classification
2015
BMC Bioinformatics
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Results and conclusions: We 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 ...
Several missing value imputation methods for gene expression data have been proposed in the literature. ...
Acknowledgements IGC was partially funded by the Excellence Initiative of the German federal and state governments and the German Research Foundation through grants GSC 111 and IZKF Aachen (Interdisciplinary ...
doi:10.1186/s12859-015-0494-3
pmid:25888091
pmcid:PMC4350881
fatcat:kkuokzdsc5antoof67qpzbbzyq
Biological impact of missing-value imputation on downstream analyses of gene expression profiles
2010
Computer applications in the biosciences : CABIOS
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Methods: Using eight data sets for differential expression (DE) and classification analysis and eight data sets for gene clustering, we demonstrate the biological impact of missing value imputation on ...
The motivation of this work is to determine the impact of missing value imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological ...
ACKNOWLEDGEMENTS GCT is partially supported by the NIH (KL2 RR024154-03) and the University of Pittsburgh (Central Research Development Fund, CRDF; Competitive Medical Research Fund, CMRF). ...
doi:10.1093/bioinformatics/btq613
pmid:21045072
pmcid:PMC3008641
fatcat:fmm3xejlr5c7dm7c6wrfcjcofy
Evaluation of missing values imputation methods in cDNA microarrays based on classification accuracy
2011
2011 1st Middle East Conference on Biomedical Engineering
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This paper focuses on studying the impact of different MV imputation methods on the classification accuracy. ...
Most of the MV imputation methods currently being used have been evaluated only in terms of the similarity between the original and imputed data. ...
The KNN-based method takes advantage of the correlation structure in microarray data by selecting genes with expression profiles similar to the gene of interest to impute missing values. ...
doi:10.1109/mecbme.2011.5752142
fatcat:iknpmcamojavfhi4rhqikzf2fi
The impact of missing values imputation methods in cDNA microarrays on downstream data analysis
2011
2011 28th National Radio Science Conference (NRSC)
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In this work the success of three MV imputation methods is measured in terms of Normalized Root Mean Square Error as well as classification accuracy and detection of differentially expressed genes (biomarkers ...
The classification accuracies computed on the original complete and imputed datasets gave a practical evaluation of the three imputation methods where it showed slight variations among them. ...
Another study considered the impact of imputation on disease classification. ...
doi:10.1109/nrsc.2011.5873605
fatcat:suqd3jfmg5herf6awshvrxqtyi
Missing value imputation improves clustering and interpretation of gene expression microarray data
2008
BMC Bioinformatics
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It can be downloaded, printed and distributed freely for any purposes (see copyright notice below). Articles in BMC journals are listed in PubMed and archived at PubMed Central. ...
Acknowledgements The work was supported by the Academy of Finland (grant 203632) and the Graduate School in Computational Biology, Bioinformatics, and Biometry (ComBi). ...
Part of the computations presented in this work were made with the help of the computing environment of the Finnish IT Center for Science (CSC). The authors thank Dr. ...
doi:10.1186/1471-2105-9-202
pmid:18423022
pmcid:PMC2386492
fatcat:vcxw4iyp45hyzn77rzktnlt6iy
Missing value imputation for gene expression data: computational techniques to recover missing data from available information
2010
Briefings in Bioinformatics
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Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. ...
In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from ...
FUNDING Hong Kong Research Grant Council (Projects CityU123408 and CityU123809). ...
doi:10.1093/bib/bbq080
pmid:21156727
fatcat:rhpy27by3rdgzld5qg553jbfqu
Impact of Missing Value Imputation on Classification for DNA Microarray Gene Expression Data—A Model-Based Study
2009
EURASIP Journal on Bioinformatics and Systems Biology
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Many missing-value (MV) imputation methods have been developed for microarray data, but only a few studies have investigated the relationship between MV imputation and classification accuracy. ...
In these cases, if data quality metrics are available, then it may be helpful to consider the data point with poor quality as missing and apply one of the most robust imputation algorithms to estimate ...
Acknowledgments This work was supported by the National Science Foundation, through NSF awards CCF-0845407 (Braga-Neto) and CCF-0634794 (Dougherty), and by the Partnership for Personalized Medicine. ...
doi:10.1155/2009/504069
pmid:20224634
pmcid:PMC3171429
fatcat:y3ugscwyfzakfbe3fao34xwpge
A comprehensive survey on computational learning methods for analysis of gene expression data in genomics
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.02958v3 fatcat:sgy65tyjhvh5tpb26r3mmchpoyOther Versions
We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and analysis of expression ...
We discuss the types of missing values and the methods and approaches usually employed in their imputation. ...
Firstly, there is only a limited knowledge on performance of different imputation methods on different types of missing data. ...
arXiv:2202.02958v4
fatcat:uipvs7ribzdondwraf64n5mzf4
Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics
2009
EURASIP Journal on Bioinformatics and Systems Biology
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Ioan Tabus for the opportunity to prepare this special issue, and the reviewers for their help and constructive criticism in preparing this special issue. ...
missing values in microarray data, and effect of imputation techniques on post genomic inference methods, RNA sequence alignment, detection of periodicity in genomic sequences and gene expression profiles ...
, clustering and classification of gene and protein expression data, and intervention in probabilistic Boolean networks. ...
doi:10.1155/2009/250306
pmid:19404479
pmcid:PMC3171422
fatcat:lxd2xajzxvgonpd7fjt7y53oaq
Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments
2010
BMC Genomics
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In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm ...
Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. ...
In the same way, we would like to thanks all the scientists who have developed and distributed missing value replacement methods. ...
doi:10.1186/1471-2164-11-15
pmid:20056002
pmcid:PMC2827407
fatcat:ox7gmqlvmnbobgqyfun5ajwoy4
Missing value imputation for epistatic MAPs
2010
BMC Bioinformatics
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We identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. ...
Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly ...
We wish to acknowledge the support of Science Foundation Ireland under Grant No. 08/SRC/I1407 (PC and DG). ...
doi:10.1186/1471-2105-11-197
pmid:20406472
pmcid:PMC2873538
fatcat:znwyuckzwva7zh3lo7mg67eggu
Enhanced SVM based Ensemble Algorithm to Improve the Classification for High Dimensional Data
2015
International Journal of Computer Applications
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Out of the various techniques of data mining, classification and clustering are two processes that have great potential in microarray data analysis. ...
The preprocessing step consists of cleaning algorithms like normalization, missing value handling routines which enhance the quality of the gene microarray data and help to improve the subsequent steps ...
gene expression data. ...
doi:10.5120/ijca2015907340
fatcat:r4oua2rthrcehcc2xukzi4ukxa
Dealing with missing values in large-scale studies: microarray data imputation and beyond
2009
Briefings in Bioinformatics
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The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels; then, we turn to other ...
After nearly a decade since the publication of the first missing value imputation methods for gene expression microarray data, new imputation approaches are still being developed at an increasing rate. ...
various downstream data analysis methods, such as unsupervised clustering of genes [6, 7] , detection of differentially expressed genes [8, 9] , supervised classification of clinical samples [10, 11 ...
doi:10.1093/bib/bbp059
pmid:19965979
fatcat:bnj6czor2rbhxdzc5noaodxqcm
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
2010
BMC Bioinformatics
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Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). ...
Conclusions: The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. ...
Gene selection itself has a huge impact on the downstream cluster analysis and both the selection method and the number of selected genes are important. ...
doi:10.1186/1471-2105-11-503
pmid:20937082
pmcid:PMC3098084
fatcat:vfqw6cjmdvg2xf7gxvkjmf3epi
A Survey on Various Disease Prediction Techniques
2018
International Journal of Trend in Scientific Research and Development
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model with missing value imputation (HPM-MI) which analyze imputation using simple k-means clustering. ...
Using gene expression pattern we predict the disease outcome and implementation of pathway based approach for classifying disease based on hyper box principles, we also present a novel hybrid prediction ...
Pathway level disease classification approach based on hyperwhere given a microarray gene expression portrait and a number of biological pathways/gene classification exactness of each pathway/gene assessed ...
doi:10.31142/ijtsrd18624
fatcat:fkoh3fk3ljb7jgd6djk7gfylhq
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