Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Integrative Systems Biology Lab
Red Sea Research Center (RSRC)
Online Publication Date2010-09-04
Print Publication Date2010-09-15
Permanent link to this recordhttp://hdl.handle.net/10754/334581
MetadataShow full item record
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.
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.
PublisherOxford University Press (OUP)
Conference/Event name19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011
PubMed Central IDPMC2935424
The following license files are associated with this item:
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.
- Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data.
- Authors: Alessio M, Cannistraci CV
- Issue date: 2016
- Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.
- Authors: Cannistraci CV, Alanis-Lobato G, Ravasi T
- Issue date: 2013 Jul 1
- Spectral embedding finds meaningful (relevant) structure in image and microarray data.
- Authors: Higgs BW, Weller J, Solka JL
- Issue date: 2006 Feb 16
- Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.
- Authors: Jamieson AR, Giger ML, Drukker K, Li H, Yuan Y, Bhooshan N
- Issue date: 2010 Jan
- DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data.
- Authors: Mrukwa G, Polanska J
- Issue date: 2022 Dec 12