Estimation of relevant variables on high-dimensional biological patterns using iterated weighted kernel functions

Rojas-Galeano, Sergio, Hsieh, Emily, Agranoff, Dan, Krishna, Sanjeev and Fernandez-Reyes, Delmiro (2008) Estimation of relevant variables on high-dimensional biological patterns using iterated weighted kernel functions. PloS one, 3 (3). e1806. ISSN 1932-6203

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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
Schools and Departments: Brighton and Sussex Medical School > Clinical Medicine
Subjects: R Medicine
R Medicine > RB Pathology
Related URLs:
Depositing User: Patricia Butler
Date Deposited: 04 Nov 2013 11:52
Last Modified: 15 Mar 2017 02:48
URI: http://sro.sussex.ac.uk/id/eprint/46904

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