Novel visualization methods for protein data

Mumtaz, Shahzad; Nabney, Ian T.; Flower, Darren R.

doi:10.1109/cibcb.2012.6217231

Cited by 2 publications

(1 citation statement)

References 49 publications

(49 reference statements)

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“…Secondly, the same sequence dataset is visualized using a nonlinear dimensionality reduction (NLDR) technique, namely Generative Topographic Mapping (GTM [12]). This technique has been applied with success to many problems in biomedicine and bioinformatics [8,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

Cárdenas

Vellido

König

et al. 2015

Genomics Comput Biol

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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.

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Section: Introductionmentioning

confidence: 99%

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

Cárdenas

Vellido

König

et al. 2015

Genomics Comput Biol

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Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences

Cárdenas

Vellido

Giraldo

2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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This brief paper addresses the problem of visually assessing the natural discriminability of the different subtypes that characterize class C G-Protein-Coupled Receptors, which are membrane proteins of interest in pharmacology, from diverse transformations of their primary amino acid sequences. Visualization, achieved using nonlinear dimensionality reduction techniques, is used as an exploratory tool to increase the interpretability of the data subtype structure. Combined with a quantitative estimation of subtype overlapping, this approach should be of help to experts in proteomics.Peer ReviewedPostprint (published version

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Novel visualization methods for protein data

Cited by 2 publications

References 49 publications

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

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

Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences

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