Novel visualization methods for protein data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations Save citation

Open

Authors

Research units

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).

Documents

  • Novel visualization methods for protein data

    Rights statement: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript, 1 MB, PDF-document

Details

Publication date2012
Publication title2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
PublisherIEEE
Pages198-205
Number of pages8
Original languageEnglish
Event2012 IEEE symposium on computational intelligence in bioinformatics and computational biology - San Diego, California, United States

Conference

Conference2012 IEEE symposium on computational intelligence in bioinformatics and computational biology
CountryUnited States
CitySan Diego, California
Period9/05/1212/05/12
OtherThis symposium will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineering and related fields. Computational Intelligence (CI) approaches include artificial neural networks and machine learning techniques, fuzzy logic, evolutionary algorithms and meta-heuristics, hybrid approaches and other emerging techniques.

Bibliographic note

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Download statistics

No data available

Employable Graduates; Exploitable Research

Copy the text from this field...