Novel visualization methods for protein data

Abstract

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
College of Health & Life Sciences > Chronic and Communicable Conditions
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Event Title: 2012 IEEE symposium on computational intelligence in bioinformatics and computational biology
Event Type: Other
Event Dates: 2012-05-09 - 2012-05-12
Uncontrolled Keywords: Electrical and Electronic Engineering
Last Modified: 02 Jan 2024 18:55
Date Deposited: 19 Mar 2012 11:57
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2012
Authors: Mumtaz, Shahzad
Nabney, Ian (ORCID Profile 0000-0003-1513-993X)
Flower, Darren (ORCID Profile 0000-0002-8542-7067)

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