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The results show that our approach outperforms other systems using the AImed dataset.
The evaluation results on the AImed dataset (Bunescu and Mooney, 2005) show that our approach outperforms other systems.
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Subdividing data according to the strongest geo-climatic gradient in each dataset aimed to reduce the strength of natural descriptors relative to land use.
We present a unique study based on extensive dataset aimed to reveal the spatiotemporal pattern of the disturbance history and role of the mixed severity disturbances in primary spruce mountain forest landscapes in the Ukrainian Carpathians.
To address this task, we developed a new approach and applied it to a dataset aimed to characterize the histone deacetylase (HDAC) Rpd3 complexes in Saccharomyces cerevisiae.
The analysis was performed in two stages: i) principal components analysis (PCA) for unsupervised analysis of the full dataset, aimed at determining whether a multivariate signal was present; ii) partial least squares discriminant analysis (PLS-DA) to help identifying the identity of the proteins responsible for the separation.
31 Our analysis is based on a unique international dataset aimed at the comparison of clinical outcomes between countries.
Consequently, for the present study we developed an alternative strategy for gene discovery from the FANTOM2 cDNA dataset aimed at identifying potential novel human disease-related genes.
Cases of colorectal cancer were defined according to codes (153.xx and 154.xx) from the International Classification of Diseases (ICD-9), and further linkage to the Catastrophic Illness Registry Dataset aimed to confirm the identification of a cancer diagnosis.
A large dataset, aimed at expanding the knowledge of OC pathology in these patients, has been recently reported by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) that represent the largest collaborative study of BRCA1 and BRCA2 mutation carriers, involving more than 37 groups from more than 20 countries [ 60].
Concurrent analysis of these datasets aimed at, for example, producing a classifier, can be performed using a range of integration strategies.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com