Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

Handle URI:
http://hdl.handle.net/10754/334581
Title:
Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
Authors:
Cannistraci, Carlo; Ravasi, Timothy ( 0000-0002-9950-465X ) ; Montevecchi, Franco Maria; Ideker, Trey; Alessio, Massimo
Abstract:
Motivation: 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.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computational Bioscience Research Center (CBRC); Integrative Systems Biology Lab; Red Sea Research Center (RSRC)
Citation:
Cannistraci 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.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Conference/Event name:
19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011
Issue Date:
Sep-2010
DOI:
10.1093/bioinformatics/btq376
PubMed ID:
20823318
PubMed Central ID:
PMC2935424
Type:
Conference Paper
ISSN:
1367-4803
Appears in Collections:
Conference Papers; Red Sea Research Center (RSRC); Computational Bioscience Research Center (CBRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCannistraci, Carloen
dc.contributor.authorRavasi, Timothyen
dc.contributor.authorMontevecchi, Franco Mariaen
dc.contributor.authorIdeker, Treyen
dc.contributor.authorAlessio, Massimoen
dc.date.accessioned2014-11-11T14:30:39Z-
dc.date.available2014-11-11T14:30:39Z-
dc.date.issued2010-09en
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.en
dc.identifier.issn1367-4803en
dc.identifier.pmid20823318en
dc.identifier.doi10.1093/bioinformatics/btq376en
dc.identifier.urihttp://hdl.handle.net/10754/334581en
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.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
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.en
dc.rightsArchived with thanks to Bioinformaticsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/en
dc.titleNonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classesen
dc.typeConference Paperen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentIntegrative Systems Biology Laben
dc.contributor.departmentRed Sea Research Center (RSRC)en
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC2935424en
dc.conference.date2011-07-17 to 2011-07-19en
dc.conference.name19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011en
dc.conference.locationVienna, AUTen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorRavasi, Timothyen
kaust.authorCannistraci, Carloen

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