Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
Type
Conference PaperKAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience Program
Computational Bioscience Research Center (CBRC)
Integrative Systems Biology Lab
Red Sea Research Center (RSRC)
Date
2010-09-04Online Publication Date
2010-09-04Print Publication Date
2010-09-15Permanent link to this record
http://hdl.handle.net/10754/334581
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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.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
BioinformaticsConference/Event name
19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011PubMed ID
20823318PubMed Central ID
PMC2935424ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btq376
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