« Previous Abstract"Synthesis of racemic 6,10,14-trimethylpentadecan-2-ol, a pheromone of rice moth and 5,9,13-trimethyltetradecanoic acid, a component of marine sponge from a common intermediate"    Next AbstractTo transform or not to transform: that is the dilemma in the statistical analysis of plant volatiles »

Plant Biol (Stuttg)

Title:		Reducing the babel in plant volatile communication: using the forest to see the trees
Author(s):		Ranganathan Y; Borges RM;
Address:		"Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India"
Journal Title:		Plant Biol (Stuttg)
Year:		2010
Volume:		12
Issue:		5
Page Number:		735 - 742
DOI:		10.1111/j.1438-8677.2009.00278.x
ISSN/ISBN:		1438-8677 (Electronic) 1435-8603 (Linking)
Abstract:		"While plants of a single species emit a diversity of volatile organic compounds (VOCs) to attract or repel interacting organisms, these specific messages may be lost in the midst of the hundreds of VOCs produced by sympatric plants of different species, many of which may have no signal content. Receivers must be able to reduce the babel or noise in these VOCs in order to correctly identify the message. For chemical ecologists faced with vast amounts of data on volatile signatures of plants in different ecological contexts, it is imperative to employ accurate methods of classifying messages, so that suitable bioassays may then be designed to understand message content. We demonstrate the utility of 'Random Forests' (RF), a machine-learning algorithm, for the task of classifying volatile signatures and choosing the minimum set of volatiles for accurate discrimination, using data from sympatric Ficus species as a case study. We demonstrate the advantages of RF over conventional classification methods such as principal component analysis (PCA), as well as data-mining algorithms such as support vector machines (SVM), diagonal linear discriminant analysis (DLDA) and k-nearest neighbour (KNN) analysis. We show why a tree-building method such as RF, which is increasingly being used by the bioinformatics, food technology and medical community, is particularly advantageous for the study of plant communication using volatiles, dealing, as it must, with abundant noise"
Keywords:		*Algorithms *Artificial Intelligence Computational Biology/*methods Ficus/*chemistry Principal Component Analysis Volatile Organic Compounds/*chemistry/classification;
Notes:		"MedlineRanganathan, Y Borges, R M eng Comparative Study Research Support, Non-U.S. Gov't England 2010/08/13 Plant Biol (Stuttg). 2010 Sep 1; 12(5):735-42. doi: 10.1111/j.1438-8677.2009.00278.x"