Dataset Information

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers.

ABSTRACT:

Motivation

Highly sensitive and specific screening tools may reduce disease -related mortality by enabling physicians to diagnose diseases in asymptomatic patients or at-risk individuals. Diagnostic tests based on multiple biomarkers may achieve the needed sensitivity and specificity to realize this clinical gain.

Results

Logic regression, a multivariable regression method predicting an outcome using logical combinations of binary predictors, yields interpretable models of the complex interactions in biologic systems. However, its performance degrades in noisy data. We extend logic regression for classification to an ensemble of logic trees (Logic Forest, LF). We conduct simulation studies comparing the ability of logic regression and LF to identify variable interactions predictive of disease status. Our findings indicate LF is superior to logic regression for identifying important predictors. We apply our method to single nucleotide polymorphism data to determine associations of genetic and health factors with periodontal disease.

Availability

LF code is publicly available on CRAN, http://cran.r-project.org/.

SUBMITTER: Wolf BJ

PROVIDER: S-EPMC3025651 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Publications

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers.

Wolf Bethany J BJ Hill Elizabeth G EG Slate Elizabeth H EH

Bioinformatics (Oxford, England) 20100713 17

<h4>Motivation</h4>Highly sensitive and specific screening tools may reduce disease -related mortality by enabling physicians to diagnose diseases in asymptomatic patients or at-risk individuals. Diagnostic tests based on multiple biomarkers may achieve the needed sensitivity and specificity to realize this clinical gain.<h4>Results</h4>Logic regression, a multivariable regression method predicting an outcome using logical combinations of binary predictors, yields interpretable models of the com ...[more]

PMID: 20628070

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Project description:BackgroundAutomated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.ResultsExperimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.ConclusionsThe characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.

Dataset Information

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers.

Motivation

Results

Availability

Publications

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers.

Similar Datasets

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