{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T22:37:36Z","timestamp":1726180656757},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031208362"},{"type":"electronic","value":"9783031208379"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20837-9_18","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T04:12:17Z","timestamp":1669349537000},"page":"227-241","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recent Dimensionality Reduction Techniques for\u00a0High-Dimensional COVID-19 Data"],"prefix":"10.1007","author":[{"given":"Ioannis L.","family":"Dallas","sequence":"first","affiliation":[]},{"given":"Aristidis G.","family":"Vrahatis","sequence":"additional","affiliation":[]},{"given":"Sotiris K.","family":"Tasoulis","sequence":"additional","affiliation":[]},{"given":"Vassilis P.","family":"Plagianakos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,26]]},"reference":[{"issue":"9","key":"18_CR1","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.210389","volume":"8","author":"JP Ioannidis","year":"2021","unstructured":"Ioannidis, J.P., Salholz-Hillel, M., Boyack, K.W., Baas, J.: The rapid, massive growth of COVID-19 authors in the scientific literature. R. Soc. Open Sci. 8(9), 210389 (2021)","journal-title":"R. Soc. Open Sci."},{"issue":"5","key":"18_CR2","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1152\/physiol.00019.2020","volume":"35","author":"MK Bohn","year":"2020","unstructured":"Bohn, M.K., Hall, A., Sepiashvili, L., Jung, B., Steele, S., Adeli, K.: Pathophysiology of COVID-19: mechanisms underlying disease severity and progression. Physiology 35(5), 288\u2013301 (2020)","journal-title":"Physiology"},{"issue":"15","key":"18_CR3","doi-asserted-by":"publisher","first-page":"10196","DOI":"10.1021\/acs.analchem.0c02060","volume":"92","author":"W Feng","year":"2020","unstructured":"Feng, W., et al.: Molecular diagnosis of COVID-19: challenges and research needs. Anal. Chem. 92(15), 10196\u201310209 (2020)","journal-title":"Anal. Chem."},{"issue":"D1","key":"18_CR4","doi-asserted-by":"publisher","first-page":"D867","DOI":"10.1093\/nar\/gkab881","volume":"50","author":"C Qi","year":"2022","unstructured":"Qi, C., et al.: SCovid: single-cell atlases for exposing molecular characteristics of COVID-19 across 10 human tissues. Nucleic Acids Res. 50(D1), D867\u2013D874 (2022)","journal-title":"Nucleic Acids Res."},{"issue":"14","key":"18_CR5","doi-asserted-by":"publisher","first-page":"8845","DOI":"10.1093\/nar\/gku555","volume":"42","author":"AE Saliba","year":"2014","unstructured":"Saliba, A.E., Westermann, A.J., Gorski, S.A., Vogel, J.: Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42(14), 8845\u20138860 (2014)","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1038\/s41591-020-0944-y","volume":"26","author":"AJ Wilk","year":"2020","unstructured":"Wilk, A.J., et al.: A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26(7), 1070\u20131076 (2020)","journal-title":"Nat. Med."},{"issue":"6","key":"18_CR7","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20188746","volume":"15","author":"MD Luecken","year":"2019","unstructured":"Luecken, M.D., Theis, F.J.: Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15(6), e8746 (2019)","journal-title":"Mol. Syst. Biol."},{"issue":"1","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-019-1898-6","volume":"20","author":"S Sun","year":"2019","unstructured":"Sun, S., Zhu, J., Ma, Y., Zhou, X.: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol. 20(1), 1\u201321 (2019)","journal-title":"Genome Biol."},{"issue":"10","key":"18_CR9","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1038\/s41588-020-0700-8","volume":"52","author":"JD Fernandes","year":"2020","unstructured":"Fernandes, J.D., et al.: The UCSC SARS-CoV-2 genome browser. Nat. Genet. 52(10), 991\u2013998 (2020)","journal-title":"Nat. Genet."},{"issue":"1","key":"18_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-017-1215-1","volume":"18","author":"Y Hasin","year":"2017","unstructured":"Hasin, Y., Seldin, M., Lusis, A.: Multi-omics approaches to disease. Genome Biol. 18(1), 1\u201315 (2017)","journal-title":"Genome Biol."},{"issue":"12","key":"18_CR11","doi-asserted-by":"publisher","DOI":"10.2196\/20756","volume":"22","author":"A Abd-Alrazaq","year":"2020","unstructured":"Abd-Alrazaq, A., et al.: Artificial intelligence in the fight against COVID-19: scoping review. J. Med. Internet Res. 22(12), e20756 (2020)","journal-title":"J. Med. Internet Res."},{"issue":"66\u201371","key":"18_CR12","first-page":"13","volume":"10","author":"L Van Der Maaten","year":"2009","unstructured":"Van Der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10(66\u201371), 13 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"2065","key":"18_CR13","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","volume":"374","author":"IT Jolliffe","year":"2016","unstructured":"Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2065), 20150202 (2016)","journal-title":"Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci."},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-13056-x","volume":"10","author":"D Kobak","year":"2019","unstructured":"Kobak, D., Berens, P.: The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10(1), 1\u201314 (2019)","journal-title":"Nat. Commun."},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"18_CR16","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"1","key":"18_CR17","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/nbt.4314","volume":"37","author":"E Becht","year":"2019","unstructured":"Becht, E., et al.: Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37(1), 38\u201344 (2019)","journal-title":"Nat. Biotechnol."},{"issue":"6","key":"18_CR18","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1038\/s41587-020-00801-7","volume":"39","author":"A Narayan","year":"2021","unstructured":"Narayan, A., Berger, B., Cho, H.: Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. 39(6), 765\u2013774 (2021)","journal-title":"Nat. Biotechnol."},{"issue":"12","key":"18_CR19","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1038\/s41587-019-0336-3","volume":"37","author":"KR Moon","year":"2019","unstructured":"Moon, K.R., et al.: Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37(12), 1482\u20131492 (2019)","journal-title":"Nat. Biotechnol."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Vrahatis, A.G., Tasoulis, S.K., Dimitrakopoulos, G.N., Plagianakos, V.P.: Visualizing high-dimensional single-cell RNA-seq data via random projections and geodesic distances. In: 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/CIBCB.2019.8791482"},{"issue":"14","key":"18_CR21","doi-asserted-by":"publisher","first-page":"1982","DOI":"10.1093\/bioinformatics\/btab041","volume":"37","author":"J Pardo-Diaz","year":"2021","unstructured":"Pardo-Diaz, J., Bozhilova, L.V., Beguerisse-D\u00edaz, M., Poole, P.S., Deane, C.M., Reinert, G.: Robust gene coexpression networks using signed distance correlation. Bioinformatics 37(14), 1982\u20131989 (2021)","journal-title":"Bioinformatics"},{"issue":"1","key":"18_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-29077-3","volume":"8","author":"F Liesecke","year":"2018","unstructured":"Liesecke, F., et al.: Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks. Sci. Rep. 8(1), 1\u201316 (2018)","journal-title":"Sci. Rep."},{"key":"18_CR23","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.48994","volume":"8","author":"AJ Tarashansky","year":"2019","unstructured":"Tarashansky, A.J., Xue, Y., Li, P., Quake, S.R., Wang, B.: Self-assembling manifolds in single-cell RNA sequencing data. Elife 8, e48994 (2019)","journal-title":"Elife"},{"issue":"9","key":"18_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3000849","volume":"18","author":"NA Lieberman","year":"2020","unstructured":"Lieberman, N.A., et al.: In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age. PLoS Biol. 18(9), e3000849 (2020)","journal-title":"PLoS Biol."},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Ng, D.L., et al.: A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood. Sci. Adv. 7(6), eabe5984 (2021)","DOI":"10.1126\/sciadv.abe5984"},{"issue":"1","key":"18_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cels.2020.10.003","volume":"12","author":"KA Overmyer","year":"2021","unstructured":"Overmyer, K.A., et al.: Large-scale multi-omic analysis of COVID-19 severity. Cell Syst. 12(1), 23\u201340 (2021)","journal-title":"Cell Syst."},{"issue":"6","key":"18_CR27","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1016\/j.cell.2020.08.002","volume":"182","author":"A Silvin","year":"2020","unstructured":"Silvin, A., et al.: Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 182(6), 1401\u20131418 (2020)","journal-title":"Cell"},{"issue":"15","key":"18_CR28","doi-asserted-by":"publisher","first-page":"3201","DOI":"10.1093\/bioinformatics\/bti517","volume":"21","author":"J Handl","year":"2005","unstructured":"Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201\u20133212 (2005)","journal-title":"Bioinformatics"},{"issue":"1","key":"18_CR29","first-page":"27","volume":"5","author":"E Rend\u00f3n","year":"2011","unstructured":"Rend\u00f3n, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27\u201334 (2011)","journal-title":"Int. J. Comput. Commun."},{"issue":"4","key":"18_CR30","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/S0165-1684(02)00475-9","volume":"83","author":"N Bolshakova","year":"2003","unstructured":"Bolshakova, N., Azuaje, F.: Cluster validation techniques for genome expression data. Signal Process. 83(4), 825\u2013833 (2003)","journal-title":"Signal Process."},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Cakir, B., Prete, M., Huang, N., Van Dongen, S., Pir, P., Kiselev, V.Y.: Comparison of visualization tools for single-cell RNAseq data. NAR Genomics Bioinform. 2(3), lqaa052 (2020)","DOI":"10.1093\/nargab\/lqaa052"}],"container-title":["Lecture Notes in Computer Science","Computational Intelligence Methods for Bioinformatics and Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20837-9_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T04:22:42Z","timestamp":1669350162000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20837-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208362","9783031208379"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20837-9_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cibb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isa.cnr.it\/cibb2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"73% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}