Dataset Information

LICRE: unsupervised feature correlation reduction for lipidomics.

ABSTRACT:

Motivation

Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective.

Results

The application of LICRE to various lipidomic datasets in diabetes and cardiovascular disease demonstrated superior discrimination in terms of the area under the receiver operator characteristic curve while using fewer lipid markers when predicting various clinical outcomes.

Availability and implementation

The MATLAB implementation of LICRE is available from http://ww2.cs.mu.oz.au/?gwong/LICRE

SUBMITTER: Wong G

PROVIDER: S-EPMC4173018 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

LICRE: unsupervised feature correlation reduction for lipidomics.

Wong Gerard G Chan Jeffrey J Kingwell Bronwyn A BA Leckie Christopher C Meikle Peter J PJ

Bioinformatics (Oxford, England) 20140613 19

<h4>Motivation</h4>Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with diseas ...[more]

PMID: 24930143

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Project description:Objective:To create an system to aid in the analysis of art history by classifying and grouping digitized paintings based on stylistic features automatically learned without prior knowledge. Material and Methods:6,776 digitized paintings from 8 different artistic styles (Art Nouveau, Baroque, Expressionism, Impressionism, Realism, Romanticism, Renaissance, and Post-Impressionism) were utilized to classify (predict) and cluster (group) paintings according to style. The method of unsupervised feature learning with K-means (UFLK), inspired by deep learning, was utilized to extract features from the paintings. These features were then used in: (1) a support vector machine algorithm to classify the style of new test paintings based on a training set of paintings having known style labels; and (2) a spectral clustering algorithm to group the paintings into distinct style groups (anonymously, without employing any known style labels). Classification performance was determined by accuracy and F-score. Clustering performance was determined by: 1) the ability to recover the original stylistic groupings (using a cost analysis of all possible combinations of 8 group label assignments); 2) F-score; and 3) a reliability analysis. The latter analysis used two novel ways to determine the distribution of the null-hypothesis: (a) a uniform distribution projected onto the principal components of the original data; and (b) a randomized, weighted adjacency matrix. The ability to gain insights into art was tested by a semantic analysis of the clustering results. For this purpose, we represented the featural characteristics of each painting by an N-dimensional feature vector, and plotted the distance between vector endpoints (i.e., similarity between paintings). Then, we color-coded the endpoints with the assigned lowest-cost style labels. The scatterplot was visually inspected for separation of the paintings, where the amount of separation between color clusters provides semantic information on the interrelatedness between styles. Results:The UFLK-extracted features resembled the edges/lines/colors in the paintings. For feature-based classification of paintings, the macro-averaged F-score was 0.469. Classification accuracy and F-score were similar/higher compared to other classification methods using more complex feature learning models (e.g., convolutional neural networks, a supervised algorithm). The clustering via UFLK-extracted features yielded 8 unlabeled style groupings. In 6 of 8 clusters, the most common true painting style matched the cluster style assigned by cost analysis. The clustering had an F-score of 0.212. (There are no comparison methods for clustering paintings.) For the semantic analysis, the featural characteristics of Baroque and Art Nouveau were found to be similar, indicating a relationship between these styles. Discussion/Conclusion:The UFLK method can extract features from digitized paintings. We were able to extract characteristics of art without any prior information about the nature of the features or the stylistic designation of the paintings. The methods herein may provide art researchers with the latest computational techniques for the documentation, interpretation, and forensics of art. The tools could assist the preservation of culturally sensitive works of art for future generations, and provide new insights into works of art and the artists who created them.

Dataset Information

LICRE: unsupervised feature correlation reduction for lipidomics.

Motivation

Results

Availability and implementation

Publications

LICRE: unsupervised feature correlation reduction for lipidomics.

Similar Datasets

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