{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:03:15Z","timestamp":1772820195954,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"S23","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      With recent advances in high-throughput technologies,\n                      <jats:italic>matrix factorization<\/jats:italic>\n                      techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering insights in the underlying biological processes. Nevertheless, current matrix factorization tools fall short in handling noisy data and missing entries, both deficiencies that are often found in real-life data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose DeepMF, a deep neural network-based factorization model. DeepMF disentangles the association between molecular feature-associated and sample-associated latent matrices, and is tolerant to noisy and missing values. It exhibited feasible cancer subtype discovery efficacy on mRNA, miRNA, and protein profiles of medulloblastoma cancer, leukemia cancer, breast cancer, and small-blue-round-cell cancer, achieving the highest clustering accuracy of 76%, 100%, 92%, and 100% respectively. When analyzing data sets with 70% missing entries, DeepMF gave the best recovery capacity with silhouette values of 0.47, 0.6, 0.28, and 0.44, outperforming other state-of-the-art MF tools on the cancer data sets Medulloblastoma, Leukemia, TCGA BRCA, and SRBCT. Its embedding strength as measured by clustering accuracy is 88%, 100%, 84%, and 96% on these data sets, which improves on the current best methods 76%, 100%, 78%, and 87%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      DeepMF demonstrated robust denoising, imputation, and embedding ability. It offers insights to uncover the underlying biological processes such as cancer subtype discovery. Our implementation of DeepMF can be found at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/paprikachan\/DeepMF\">https:\/\/github.com\/paprikachan\/DeepMF<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-019-3291-6","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T03:02:42Z","timestamp":1577415762000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["DeepMF: deciphering the latent patterns in omics profiles with a deep learning method"],"prefix":"10.1186","volume":"20","author":[{"given":"Lingxi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jiao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Shuai Cheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"issue":"10","key":"3291_CR1","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.tig.2018.07.003","volume":"34","author":"Genevieve L. Stein-O\u2019Brien","year":"2018","unstructured":"Stein-O\u2019Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX, Greene CS, Goff LA, Li Y, Ngom A, Ochs MF, et al. Enter the matrix: factorization uncovers knowledge from omics. Trends Genet. 2018. https:\/\/doi.org\/10.1016\/j.tig.2018.07.003.","journal-title":"Trends in Genetics"},{"issue":"12","key":"3291_CR2","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1073\/pnas.0308531101","volume":"101","author":"J-P Brunet","year":"2004","unstructured":"Brunet J-P, Tamayo P, Golub TR, Mesirov JP. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci. 2004; 101(12):4164\u20139.","journal-title":"Proc Natl Acad Sci"},{"key":"3291_CR3","doi-asserted-by":"publisher","unstructured":"Hu F, Zhou Y, Wang Q, Yang Z, Shi Y, Chi Q. Gene expression classification of lung adenocarcinoma into molecular subtypes. IEEE\/ACM Trans Comput Biol Bioinform. 2019. https:\/\/doi.org\/10.1109\/tcbb.2019.2905553.","DOI":"10.1109\/tcbb.2019.2905553"},{"issue":"23","key":"3291_CR4","doi-asserted-by":"publisher","first-page":"9125","DOI":"10.1158\/0008-5472.CAN-09-1709","volume":"69","author":"MF Ochs","year":"2009","unstructured":"Ochs MF, Rink L, Tarn C, Mburu S, Taguchi T, Eisenberg B, Godwin AK. Detection of treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data. Cancer Res. 2009; 69(23):9125\u201332.","journal-title":"Cancer Res"},{"key":"3291_CR5","doi-asserted-by":"publisher","unstructured":"Ochs MF, Fertig EJ. Matrix factorization for transcriptional regulatory network inference. In: 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE: 2012. p. 387\u201396. https:\/\/doi.org\/10.1109\/cibcb.2012.6217256.","DOI":"10.1109\/cibcb.2012.6217256"},{"issue":"3","key":"3291_CR6","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TNB.2013.2263390","volume":"12","author":"EJ Fertig","year":"2013","unstructured":"Fertig EJ, Favorov AV, Ochs MF. Identifying context-specific transcription factor targets from prior knowledge and gene expression data. IEEE Trans Nanobioscience. 2013; 12(3):142\u20139.","journal-title":"IEEE Trans Nanobioscience"},{"issue":"1","key":"3291_CR7","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.celrep.2012.12.008","volume":"3","author":"LB Alexandrov","year":"2013","unstructured":"Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 2013; 3(1):246\u201359.","journal-title":"Cell Rep"},{"issue":"7463","key":"3291_CR8","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/nature12477","volume":"500","author":"LB Alexandrov","year":"2013","unstructured":"Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, B\u00f8rresen-Dale A-L, et al. Signatures of mutational processes in human cancer. Nature. 2013; 500(7463):415.","journal-title":"Nature"},{"key":"3291_CR9","doi-asserted-by":"publisher","unstructured":"Alexandrov L, Kim J, Haradhvala NJ, Huang MN, Ng AW, Boot A, Covington KR, Gordenin DA, Bergstrom E, Lopez-Bigas N, et al. The repertoire of mutational signatures in human cancer. BioRxiv. 2018:322859. https:\/\/doi.org\/10.1101\/322859.","DOI":"10.1101\/322859"},{"issue":"11","key":"3291_CR10","doi-asserted-by":"publisher","first-page":"78127","DOI":"10.1371\/journal.pone.0078127","volume":"8","author":"EJ Fertig","year":"2013","unstructured":"Fertig EJ, Markovic A, Danilova LV, Gaykalova DA, Cope L, Chung CH, Ochs MF, Califano JA. Preferential activation of the hedgehog pathway by epigenetic modulations in hpv negative hnscc identified with meta-pathway analysis. PLoS ONE. 2013; 8(11):78127.","journal-title":"PLoS ONE"},{"issue":"11","key":"3291_CR11","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1038\/nmeth.2651","volume":"10","author":"M Hofree","year":"2013","unstructured":"Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nat Methods. 2013; 10(11):1108.","journal-title":"Nat Methods"},{"key":"3291_CR12","doi-asserted-by":"publisher","first-page":"6591","DOI":"10.1038\/srep06591","volume":"4","author":"W Zhao","year":"2014","unstructured":"Zhao W, Luo J, Jiao S. Comprehensive characterization of cancer subtype associated long non-coding rnas and their clinical implications. Sci Rep. 2014; 4:6591.","journal-title":"Sci Rep"},{"issue":"11","key":"3291_CR13","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/gb-2003-4-11-r76","volume":"4","author":"S-I Lee","year":"2003","unstructured":"Lee S-I, Batzoglou S. Application of independent component analysis to microarrays. Genome Biol. 2003; 4(11):76.","journal-title":"Genome Biol"},{"issue":"1","key":"3291_CR14","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1186\/1471-2105-11-367","volume":"11","author":"R Gaujoux","year":"2010","unstructured":"Gaujoux R, Seoighe C. A flexible r package for nonnegative matrix factorization. BMC Bioinformatics. 2010; 11(1):367.","journal-title":"BMC Bioinformatics"},{"issue":"21","key":"3291_CR15","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.1093\/bioinformatics\/btq503","volume":"26","author":"EJ Fertig","year":"2010","unstructured":"Fertig EJ, Ding J, Favorov AV, Parmigiani G, Ochs MF. Cogaps: an r\/c++ package to identify patterns and biological process activity in transcriptomic data. Bioinformatics. 2010; 26(21):2792\u20133.","journal-title":"Bioinformatics"},{"issue":"6","key":"3291_CR16","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1038\/bjc.2013.496","volume":"109","author":"C Wilhelm-Benartzi","year":"2013","unstructured":"Wilhelm-Benartzi C, Koestler D, Karagas M, Flanagan J, Christensen B, Kelsey K, Marsit C, Houseman E, Brown R. Review of processing and analysis methods for dna methylation array data. British J Cancer. 2013; 109(6):1394.","journal-title":"British J Cancer"},{"issue":"2","key":"3291_CR17","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1093\/bib\/bbp059","volume":"11","author":"T Aittokallio","year":"2009","unstructured":"Aittokallio T. Dealing with missing values in large-scale studies: microarray data imputation and beyond. Brief Bioinform. 2009; 11(2):253\u201364.","journal-title":"Brief Bioinform"},{"key":"3291_CR18","doi-asserted-by":"publisher","unstructured":"Xue H-J, Dai X, Zhang J, Huang S, Chen J. Deep matrix factorization models for recommender systems. In: IJCAI: 2017. p. 3203\u20139. https:\/\/doi.org\/10.24963\/ijcai.2017\/447.","DOI":"10.24963\/ijcai.2017\/447"},{"key":"3291_CR19","doi-asserted-by":"publisher","first-page":"012001","DOI":"10.1088\/1742-6596\/1060\/1\/012001","volume":"1060","author":"Fei Zhang","year":"2018","unstructured":"Zhang F, Song J, Peng S. Deep matrix factorization for recommender systems with missing data not at random. In: Journal of Physics: Conference Series, vol. 1060. IOP Publishing: 2018. p. 012001. https:\/\/doi.org\/10.1088\/1742-6596\/1060\/1\/012001.","journal-title":"Journal of Physics: Conference Series"},{"issue":"7","key":"3291_CR20","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7):47.","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"3291_CR21","doi-asserted-by":"publisher","first-page":"1005752","DOI":"10.1371\/journal.pcbi.1005752","volume":"13","author":"F Rohart","year":"2017","unstructured":"Rohart F, Gautier B, Singh A, L\u00ea Cao K-A. mixomics: An r package for \u2019omics feature selection and multiple data integration. PLoS Comput Biol. 2017; 13(11):1005752.","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"3291_CR22","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001; 7(6):673.","journal-title":"Nat Med"},{"issue":"1","key":"3291_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v025.i01","volume":"25","author":"S L\u00ea","year":"2008","unstructured":"L\u00ea S, Josse J, Husson F, et al. Factominer: an r package for multivariate analysis. J Stat Softw. 2008; 25(1):1\u201318.","journal-title":"J Stat Softw"},{"issue":"0","key":"3291_CR24","first-page":"1","volume":"1","author":"J Marchini","year":"2013","unstructured":"Marchini J, Heaton C, Ripley B. fastica: Fastica algorithms to perform ica and projection pursuit. R Packag Vers. 2013; 1(0):1.","journal-title":"R Packag Vers"},{"issue":"6","key":"3291_CR25","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","volume":"17","author":"O Troyanskaya","year":"2001","unstructured":"Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for dna microarrays. Bioinformatics. 2001; 17(6):520\u20135.","journal-title":"Bioinformatics"},{"key":"3291_CR26","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987; 20:53\u201365.","journal-title":"J Comput Appl Math"},{"issue":"336","key":"3291_CR27","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand WM. Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971; 66(336):846\u201350.","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"3291_CR28","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1186\/s12859-016-1122-6","volume":"17","author":"D Lin","year":"2016","unstructured":"Lin D, Zhang J, Li J, Xu C, Deng H-W, Wang Y-P. An integrative imputation method based on multi-omics datasets. BMC Bioinformatics. 2016; 17(1):247.","journal-title":"BMC Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3291-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3291-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3291-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T19:06:23Z","timestamp":1608923183000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3291-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":28,"journal-issue":{"issue":"S23","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3291"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3291-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/744706","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"27 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"648"}}