Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods

  • Martha Ivon Cardenas Universitat Politècnica de Catalunya
  • Alfredo Vellido Universitat Politècnica de Catalunya
  • Caroline König Universitat Politècnica de Catalunya
  • René Alquezar Universitat Politècnica de Catalunya
  • Jesús Giraldo Universitat Autònoma de Barcelona

Abstract

G-protein-coupled receptors are cell membrane proteins of great interest in biology and pharmacology. Previous analysis of Class C of these receptors has revealed the existence of an upper boundary on the accuracy that can be achieved in the classification of their standard subtypes from the unaligned transformation of their primary sequences. To further investigate this apparent boundary, the focus of the analysis in this paper is placed on receptor sequences that were previously misclassified using supervised learning methods. In our experiments, these sequences are visualized using a nonlinear dimensionality reduction technique and phylogenetic trees. They are subsequently characterized against the rest of the data and, particularly, against the rest of cases of their own subtype. This exploratory visualization should help us to discriminate between different types of misclassification and to build hypotheses about database quality problems and the extent to which GPCR sequence transformations limit subtype discriminability. The reported experiments provide a proof of concept for the proposed method.

Published
2015-09-18
How to Cite
CARDENAS, Martha Ivon et al. Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods. Genomics and Computational Biology, [S.l.], v. 1, n. 1, p. e19, sep. 2015. ISSN 2365-7154. Available at: <https://genomicscomputbiol.org/ojs3/GCB/article/view/32>. Date accessed: 25 june 2019. doi: https://doi.org/10.18547/gcb.2015.vol1.iss1.e19.
Section
Research Articles