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dc.contributor.authorCannistraci, Carlo
dc.contributor.authorRavasi, Timothy
dc.contributor.authorMontevecchi, Franco Maria
dc.contributor.authorIdeker, Trey
dc.contributor.authorAlessio, Massimo
dc.date.accessioned2014-11-11T14:30:39Z
dc.date.available2014-11-11T14:30:39Z
dc.date.issued2010-09
dc.identifier.citationCannistraci CV, Ravasi T, Montevecchi FM, Ideker T, Alessio M (2010) Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes. Bioinformatics 26: i531-i539. doi:10.1093/bioinformatics/btq376.
dc.identifier.issn1367-4803
dc.identifier.pmid20823318
dc.identifier.doi10.1093/bioinformatics/btq376
dc.identifier.urihttp://hdl.handle.net/10754/334581
dc.description.abstractMotivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. Methods: 'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. © The Author(s) 2010. Published by Oxford University Press.
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rightsArchived with thanks to Bioinformatics
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/
dc.titleNonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
dc.typeConference Paper
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentIntegrative Systems Biology Lab
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC2935424
dc.conference.date2011-07-17 to 2011-07-19
dc.conference.name19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011
dc.conference.locationVienna, AUT
dc.eprint.versionPublisher's Version/PDF
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personRavasi, Timothy
kaust.personCannistraci, Carlo
refterms.dateFOA2018-06-14T04:39:16Z


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.