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We also showed that our method is dataset independent and can apply to ChIP-seq data and the E. coli genome.
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The most useful resources for computational methods are datasets of known interactions, often used as training data, positive control data or benchmark data in analyses.
The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes.
Indeed, the benefit of our method is substantiated for datasets from preclinical or clinical studies where subject-to-subject variations are relatively large.
Our method is scalable to datasets with a large number of annotations.
Our method is validated on synthetic datasets and a large scale CT dataset with several thousands of images.
The proposed method is tested over a dataset of hands from 100 subjects.
The proposed method is tested against a dataset created from public DNA sequences provided by InSNP.
The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets).
The proposed method is tested on two datasets: ISO New England data and North American electric utility data.
The feature selection method is then assessed on datasets of the UCI Machine Learning Repository.
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