Ensemble classifier method
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One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited.
19p vialfrednobel 29-01-2022 13 0 Download
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The specific objectives were: 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3) to evaluate the performance of the ensemble classifier.
31p spiritedaway36 28-11-2021 16 4 Download
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In silico investigations on the integration of multiple datasets are in need of higher statistical power methods to unveil secondary findings that were hidden from the initial analyses. We present here a novel method for the network analysis of messenger RNA post-translational regulation by microRNA molecules.
11p vitzuyu2711 29-09-2021 8 1 Download
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Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.
13p vioklahoma2711 19-11-2020 14 2 Download
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Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands
12p vioklahoma2711 19-11-2020 11 0 Download
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Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions.
10p vioklahoma2711 19-11-2020 10 1 Download
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Various methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explanatory features, each of which may be composed of individually insignificant variables.
14p vicolorado2711 22-10-2020 18 0 Download
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In the second method, instead of learning on each classifier separately as in the former, we combine these classifiers by a voting ensemble. The experimental results on 20 benchmark imbalanced datasets collected from the UCI repository show that our methods significantly outperform the baseline NB. These methods also perform as good as the state-of-the-art sampling methods and significantly better in certain cases.
13p dannisa 14-12-2018 22 0 Download