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

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posted on 2023-06-08, 16:15 authored by Sergio Rojas-Galeano, Emily Hsieh, Dan Agranoff, Sanjeev Krishna, Delmiro Fernandez-Reyes
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

PLoS ONE

ISSN

1932-6203

Publisher

Public Library of Science

Issue

3

Volume

3

Article number

e1806

Department affiliated with

  • Clinical and Experimental Medicine Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2013-11-04

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2017-03-15

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