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

• Published in: PloS one 2008-03, Vol.3 (3), p.e1806-e1806
• Main Authors: Rojas-Galeano, Sergio, Hsieh, Emily, Agranoff, Dan, Krishna, Sanjeev, Fernandez-Reyes, Delmiro
• Format: Article
• Language: English
• Subjects: African trypanosomiasis; Algorithms; Analysis; Artificial intelligence; Cancer; Classification; Combinatorial analysis; Computational Biology - statistics & numerical data; Computational Biology/Systems Biology; Computer Science; Datasets; DNA microarrays; Experimentation; Gene expression; Genetic algorithms; Genomics - statistics & numerical data; Humans; Infectious Diseases; Infectious Diseases/Neglected Tropical Diseases; Infectious Diseases/Tropical and Travel-Associated Diseases; Information storage; Integral equations; Kernel functions; Malaria; Mathematical analysis; Medical research; Methods; Mycobacterium; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Parasitology; Pattern Recognition, Automated; Proteomics; Proteomics - statistics & numerical data; Redundancy; Software; Stochasticity; Tuberculosis; Variables; Vector-borne diseases
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0001806

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. 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. 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.