Novel visualization methods for protein data

Shahzad Mumtaz, Ian Nabney, Darren Flower

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

2 Citations (Scopus)
11 Downloads (Pure)

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).
Original languageEnglish
Title of host publication2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Place of PublicationUnited States
PublisherIEEE Computer Society
Pages198-205
Number of pages8
Publication statusPublished - 2012

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