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Impact of missing data imputation methods on gene expression clustering and classification

Marcilio CP de Souto, Pablo A Jaskowiak, Ivan G Costa
2015 BMC Bioinformatics  
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

Sunghee Oh, Dongwan D. Kang, Guy N. Brock, George C. Tseng
2010 Computer applications in the biosciences : CABIOS  
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

Vidan Fathi Ghoneim, Nahed H. Solouma, Yasser M. Kadah
2011 2011 1st Middle East Conference on Biomedical Engineering  
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

Vidan Fathi Ghoneim, Nahed H. Solouma, Yasser M. Kadah
2011 2011 28th National Radio Science Conference (NRSC)  
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

Johannes Tuikkala, Laura L Elo, Olli S Nevalainen, Tero Aittokallio
2008 BMC Bioinformatics  
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

A. W.-C. Liew, N.-F. Law, H. Yan
2010 Briefings in Bioinformatics  
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

Youting Sun, Ulisses Braga-Neto, EdwardR Dougherty
2009 EURASIP Journal on Bioinformatics and Systems Biology  
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]

Nikita Bhandari, Rahee Walambe, Ketan Kotecha, Satyajeet Khare
2022 arXiv   pre-print
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

Erchin Serpedin, Javier Garcia-Frias, Yufei Huang, Ulisses Braga-Neto
2009 EURASIP Journal on Bioinformatics and Systems Biology  
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

Magalie Celton, Alain Malpertuy, Gaelle Lelandais, Alexandre G. de Brevern
2010 BMC Genomics  
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

Colm Ryan, Derek Greene, Gerard Cagney, Pádraig Cunningham
2010 BMC Bioinformatics  
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

Kavitha S., M. Hemalatha
2015 International Journal of Computer Applications  
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

T. Aittokallio
2009 Briefings in Bioinformatics  
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

Eva Freyhult, Mattias Landfors, Jenny Önskog, Torgeir R Hvidsten, Patrik Rydén
2010 BMC Bioinformatics  
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

C. Leancy Jannet, G. Sumalatha
2018 International Journal of Trend in Scientific Research and Development  
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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