{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T16:04:40Z","timestamp":1750349080539},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.<\/jats:p><jats:p>Results: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.<\/jats:p><jats:p>Availability: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.<\/jats:p><jats:p>Contact: \u00a0dwkim@cau.ac.kr<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl555","type":"journal-article","created":{"date-parts":[[2006,11,1]],"date-time":"2006-11-01T04:03:05Z","timestamp":1162353785000},"page":"107-113","source":"Crossref","is-referenced-by-count":15,"title":["Towards clustering of incomplete microarray data without the use of imputation"],"prefix":"10.1093","volume":"23","author":[{"given":"Dae-Won","family":"Kim","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Chung-Ang University 1 \u00a0 1 \u00a0 \u00a0 Seoul City, Republic of Korea"}]},{"given":"Ki-Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of California at San Diego 2 \u00a0 2 \u00a0 \u00a0 California, USA"}]},{"given":"Kwang H.","family":"Lee","sequence":"additional","affiliation":[{"name":"Advanced Information Technology Research Center, KAIST 3 \u00a0 3 \u00a0 \u00a0 Daejeon, Republic of Korea"}]},{"given":"Doheon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of BioSystems, KAIST 4 \u00a0 4 \u00a0 \u00a0 Daejeon, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2006,10,31]]},"reference":[{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene Ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. Genet."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"e34","DOI":"10.1093\/nar\/gnh026","article-title":"LSimpute: accurate estimation of missing values in microarray data with least square methods","volume":"32","author":"Bo","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S1097-2765(00)80114-8","article-title":"A genome-wide transcriptional analysis of the mitotic cell cycle","volume":"2","author":"Cho","year":"1998","journal-title":"Mol. Cell"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1126\/science.282.5389.699","article-title":"The transcriptional program of sporulation in budding yeast","volume":"282","author":"Chu","year":"1998","journal-title":"Science"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1093\/bioinformatics\/btg025","article-title":"Comparions and validation of statistical clustering techniques for microarray gene expression data","volume":"19","author":"Datta","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/bioinformatics\/btg119","article-title":"Fuzzy c-means method for clustering microarray data","volume":"19","author":"Dembele","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","first-page":"257","article-title":"Exploring the metabolic and genetic control of gene expression on a genomic scale","volume":"282","author":"DeRisi","year":"1997","journal-title":"Science"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1093\/bioinformatics\/btg209","article-title":"Diametrical clustering for identifying anti-correlated gene clusters","volume":"19","author":"Dhilon","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1093\/bioinformatics\/btg038","article-title":"Bagging to improve the accuracy of a clustering procedure","volume":"19","author":"Dudoit","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","article-title":"Cluster analysis and display of genome-wide expression patterns","volume":"95","author":"Eisen","year":"1998","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023041105102390000_","unstructured":"Fuschik M.E. Methods for knowledge discovery in microarray data 2003 Ph.D. Thesis, University of Otago, Dunedin, New Zealand"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1101\/gr.397002","article-title":"Judging the quality of gene expression-based clustering methods using gene annotation","volume":"12","author":"Gibbons","year":"2002","journal-title":"Genome Res."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/3477.956035","article-title":"Fuzzy c-means clustering of incomplete data","volume":"31","author":"Hathaway","year":"2001","journal-title":"IEEE Trans. Sys. Man Cybernet. B: Cybernetics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1093\/bioinformatics\/btg053","article-title":"Novel clustering algorithm for microarray expression data in a truncated SVD space","volume":"19","author":"Horn","year":"2003","journal-title":"Boinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0076-6879(02)50972-1","article-title":"Saccharomyces Genome Database","volume":"350","author":"Issel-Tarver","year":"2002","journal-title":"Methods Enzymol."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1093\/bioinformatics\/bti251","article-title":"Detecting clusters of different geometrical shapes in microarray gene expression data","volume":"21","author":"Kim","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1093\/bioinformatics\/17.5.405","article-title":"Analysis of temporal gene expression profiles: clustering by simuulated annealing and determining the optimal number of clusters","volume":"17","author":"Lukashin","year":"2001","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1093\/bioinformatics\/bth007","article-title":"Guassian mixture clustering and imputation of microarray data","volume":"20","author":"Ouyang","year":"2004","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1093\/bioinformatics\/btg288","article-title":"Kernel hierarchical gene clustering from microarray gene expression data","volume":"19","author":"Qin","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","first-page":"284","article-title":"K-means type algorithms: a generalized convergence theorem and the caracterization of local optimality","volume":"6","author":"Selim","year":"1984","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1093\/bioinformatics\/btg232","article-title":"CLICK and EXPANDER: a system for clustering and visualizing gene expression data","volume":"19","author":"Sharan","year":"2003","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"S231","DOI":"10.1093\/bioinformatics\/18.suppl_2.S231","article-title":"The mutual information: detecting and evaluating dependencies between variables","volume":"18","author":"Steuer","year":"2002","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1073\/pnas.96.6.2907","article-title":"Interpreting patters of gene expression with self-organizing maps: methods and application to hematopoietic differentiation","volume":"96","author":"Tamayo","year":"1999","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1038\/10343","article-title":"Systematic determination of genetic network architecture","volume":"22","author":"Tavazoie","year":"1999","journal-title":"Nat. Genet."},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","first-page":"309","article-title":"Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees","volume":"17","author":"Xu","year":"2001","journal-title":"Bioinformatics"},{"key":"2023041105102390000_","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/17.4.309","article-title":"Validating clustering for gene expression data","volume":"17","author":"Yeung","year":"2001","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/1\/107\/49816333\/bioinformatics_23_1_107.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/23\/1\/107\/49816333\/bioinformatics_23_1_107.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T23:16:35Z","timestamp":1707347795000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/23\/1\/107\/189651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,10,31]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2007,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btl555","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2007,1,1]]},"published":{"date-parts":[[2006,10,31]]}}}