Rojas-Galeano, S; Hsieh, E; Agranoff, D; Krishna, S; Fernandez-Reyes, D
(2008)
Estimation of Relevant Variables on High-Dimensional Biological Patterns Using Iterated Weighted Kernel Functions.
PLOS ONE, 3 (3).
e1806 (1) - e1806 (11).
ISSN 1932-6203
https://doi.org/10.1371/journal.pone.0001806
SGUL Authors: Krishna, Sanjeev
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Abstract
Background: The analysis of complex proteomic and genomic profiles involves the identification of significant markers
within a set of hundreds or even thousands of variables that represent a high-dimensional problem space. The occurrence of noise, redundancy or combinatorial interactions in the profile makes the selection of relevant variables harder.
Methodology/Principal Findings: Here we propose a method to select variables based on estimated relevance to hidden
patterns. Our method combines a weighted-kernel discriminant with an iterative stochastic probability estimation algorithm to discover the relevance distribution over the set of variables. We verified the ability of our method to select predefined relevant variables in synthetic proteome-like data and then assessed its performance on biological high-dimensional problems. Experiments were run on serum proteomic datasets of infectious diseases. The resulting variable subsets achieved classification accuracies of 99% on Human African Trypanosomiasis, 91% on Tuberculosis, and 91% on Malaria serum proteomic profiles with fewer than 20% of variables selected. Our method scaled-up to dimensionalities of much higher orders of magnitude as shown with gene expression microarray datasets in which we obtained classification accuracies close to 90% with fewer than 1% of the total number of variables.
Conclusions: Our method consistently found relevant variables attaining high classification accuracies across synthetic and biological datasets. Notably, it yielded very compact subsets compared to the original number of variables, which should simplify downstream biological experimentation.
Item Type: |
Article
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Additional Information: |
Copyright: 2008 Rojas-Galeano et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited |
Keywords: |
Algorithms, Computational Biology, Genomics, Humans, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Proteomics, Software, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MULTIDISCIPLINARY SCIENCES, General Science & Technology, MD Multidisciplinary |
SGUL Research Institute / Research Centre: |
Academic Structure > Infection and Immunity Research Institute (INII) |
Journal or Publication Title: |
PLOS ONE |
ISSN: |
1932-6203 |
Related URLs: |
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Dates: |
Date | Event |
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26 March 2008 | Published |
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Web of Science ID: |
WOS:000260762400001 |
URI: |
https://openaccess.sgul.ac.uk/id/eprint/107065 |
Publisher's version: |
https://doi.org/10.1371/journal.pone.0001806 |
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