{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T02:35:08Z","timestamp":1649039708164},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"S17","license":[{"start":{"date-parts":[[2012,12,1]],"date-time":"2012-12-01T00:00:00Z","timestamp":1354320000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as much as possible. However, it is not trivial to apply it to genome-scale data (e.g., microarray gene expression profiles) on regular desktop computers, because of its high computational complexity.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We implemented a highly-efficient software application, called CFMDS (CUDA-based Fast MultiDimensional Scaling), which produces an approximate solution of the classical MDS based on CUDA (compute unified device architecture) and the divide-and-conquer principle. CUDA is a parallel computing architecture exploiting the power of the GPU (graphics processing unit). The principle of divide-and-conquer was adopted for circumventing the small memory problem of usual graphics cards. Our application software has been tested on various benchmark datasets including microarrays and compared with the classical MDS algorithms implemented using C# and MATLAB. In our experiments, CFMDS was more than a hundred times faster for large data than such general solutions. Regarding the quality of dimensionality reduction, our approximate solutions were as good as those from the general solutions, as the Pearson's correlation coefficients between them were larger than 0.9.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>CFMDS is an expeditious solution for the data dimensionality reduction problem. It is especially useful for efficient processing of genome-scale data consisting of several thousands of objects in several minutes.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-13-s17-s23","type":"journal-article","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T01:59:13Z","timestamp":1576029553000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CFMDS: CUDA-based fast multidimensional scaling for genome-scale data"],"prefix":"10.1186","volume":"13","author":[{"given":"Sungin","family":"Park","sequence":"first","affiliation":[]},{"given":"Soo-Yong","family":"Shin","sequence":"additional","affiliation":[]},{"given":"Kyu-Baek","family":"Hwang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,12,13]]},"reference":[{"key":"5487_CR1","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1101\/gr.2538704","volume":"14","author":"P Khaitovich","year":"2004","unstructured":"Khaitovich P, Muetzel B, She X, Lachmann M, Hellmann I, Dietzsch J, Steigele S, Do HH, Weiss G, Enard W, Heissig F, Arendt T, Nieselt-Struwe K, Eichler EE, P\u0101\u0101bo S: Regional patterns of gene expression in human and chimpanzee brains. Genome Res. 2004, 14: 1462-1473. 10.1101\/gr.2538704.","journal-title":"Genome Res"},{"issue":"6","key":"5487_CR2","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1093\/bioinformatics\/bti067","volume":"21","author":"YH Taguchi","year":"2005","unstructured":"Taguchi YH, Oono Y: Relational patterns of gene expression via non-metric multidimensional scaling analysis. Bioinformatics. 2005, 21 (6): 730-740. 10.1093\/bioinformatics\/bti067.","journal-title":"Bioinformatics"},{"issue":"5","key":"5487_CR3","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1038\/nmeth1032","volume":"4","author":"LH Loo","year":"2007","unstructured":"Loo LH, Wu LF, Altschuler SJ: Image-based multivariate profiling of drug responses from single cells. Nat Methods. 2007, 4 (5): 445-453.","journal-title":"Nat Methods"},{"key":"5487_CR4","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1186\/1471-2105-10-215","volume":"10","author":"S Gowrisankar","year":"2009","unstructured":"Gowrisankar S, Jegga AG: Regression based predictor for p53 transactivation. BMC Bioinformatics. 2009, 10: 215-10.1186\/1471-2105-10-215.","journal-title":"BMC Bioinformatics"},{"key":"5487_CR5","volume-title":"Modern Multidimensional Scaling: Theory and Applications","author":"I Borg","year":"2005","unstructured":"Borg I, Groenen PJF: Modern Multidimensional Scaling: Theory and Applications. 2005, New York, Springer, 2","edition":"2"},{"key":"5487_CR6","volume-title":"Proceedings of the ECCV 2006 Workshop on Computational Intensive Methods for Computer Vision","author":"T Yang","year":"2006","unstructured":"Yang T, Lui J, McMillan L, Wang W: A fast approximation to multidimensional scaling. Proceedings of the ECCV 2006 Workshop on Computational Intensive Methods for Computer Vision. 2006"},{"key":"5487_CR7","volume-title":"Proceedings of SIGGRAPH '05 ACM SIGGRAPH 2005 Courses","author":"M Harris","year":"2005","unstructured":"Harris M: Mapping computational concepts to GPUs. Proceedings of SIGGRAPH '05 ACM SIGGRAPH 2005 Courses. 2005"},{"key":"5487_CR8","unstructured":"NVIDIA CUDA Zone. [http:\/\/www.nvidia.com\/object\/cuda%20home%20new.html]"},{"key":"5487_CR9","unstructured":"CULA tools, EM Photonics. [http:\/\/www.culatools.com]"},{"issue":"Suppl 2","key":"5487_CR10","doi-asserted-by":"publisher","first-page":"S10","DOI":"10.1186\/1471-2105-9-S2-S10","volume":"9","author":"SA Manavski","year":"2008","unstructured":"Manavski SA, Valle G: CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinformatics. 2008, 9 (Suppl 2): S10-10.1186\/1471-2105-9-S2-S10.","journal-title":"BMC Bioinformatics"},{"key":"5487_CR11","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1186\/1471-2105-9-377","volume":"9","author":"A Wirawan","year":"2008","unstructured":"Wirawan A, Kwoh CK, Hieu NT, Schmidt B: CBESW: Sequence alignment on the Playstation 3. BMC Bioinformatics. 2008, 9: 377-10.1186\/1471-2105-9-377.","journal-title":"BMC Bioinformatics"},{"key":"5487_CR12","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/1756-0500-2-73","volume":"2","author":"Y Lui","year":"2009","unstructured":"Lui Y, Maskell DL, Schmidt B: CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units. BMC Research Notes. 2009, 2: 73-10.1186\/1756-0500-2-73.","journal-title":"BMC Research Notes"},{"key":"5487_CR13","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1186\/1471-2105-11-446","volume":"11","author":"AD Stivala","year":"2010","unstructured":"Stivala AD, Stuckey PJS, Wirth AI: Fast and accurate protein substructure searching with simulated annealing and GPUs. BMC Bioinformatics. 2010, 11: 446-10.1186\/1471-2105-11-446.","journal-title":"BMC Bioinformatics"},{"key":"5487_CR14","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1186\/1471-2105-11-329","volume":"11","author":"ID Shterev","year":"2010","unstructured":"Shterev ID, Jung SH, George SL, Owzar K: permGPU: Using graphics processing units in RNA microarray association studies. BMC Bioinformatics. 2010, 11: 329-10.1186\/1471-2105-11-329.","journal-title":"BMC Bioinformatics"},{"key":"5487_CR15","unstructured":"Fester T, Schreiber F, Strickert M: CUDA-based multi-core implementation of MDS-based bioinformatics algorithms. Proceedings of German Conference on Bioinformatics (GCB 2009). 67-79."},{"key":"5487_CR16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/1471-2105-9-179","volume":"9","author":"J Tzeng","year":"2008","unstructured":"Tzeng J, Lu HHS, Li WH: Multidimensional scaling for large genomic datasets. BMC Bioinformatics. 2008, 9: 179-10.1186\/1471-2105-9-179.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"5487_CR17","first-page":"427","volume":"16","author":"S Park","year":"2010","unstructured":"Park S, Hwang KB: An efficient multidimensional scaling method based on CUDA and divide-and-conquer. Journal of the Korean Institute of Information Scientists and Engineers: Computing Practices and Letters. 2010, 16 (4): 427-431.","journal-title":"Journal of the Korean Institute of Information Scientists and Engineers: Computing Practices and Letters"},{"key":"5487_CR18","volume-title":"Sparse multidimensional scaling using landmark points","author":"V De Silva","year":"2004","unstructured":"De Silva V, Tenenbaum JB: Sparse multidimensional scaling using landmark points. 2004, Technical Report, Stanford University"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-13-S17-S23.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/1471-2105-13-S17-S23\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-13-S17-S23.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T21:07:09Z","timestamp":1630530429000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-13-S17-S23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,12]]},"references-count":18,"journal-issue":{"issue":"S17","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["5487"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-13-s17-s23","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,12]]},"assertion":[{"value":"13 December 2012","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"S23"}}