Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

Estimation of Relevant Variables on High-Dimensional Biological Patterns Using Iterated Weighted Kernel Functions

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
SGUL Authors: Krishna, Sanjeev

["document_typename_application/pdf; charset=binary" not defined] Published Version
Available under License St George's repository terms & conditions.

Download (431kB) | Preview


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
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:
26 March 2008Published
Web of Science ID: WOS:000260762400001
Publisher's version:

Actions (login required)

Edit Item Edit Item