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Table 1 Comparison of the benchmark and existing 3D saliency detection datasets Name Size Object no.
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In this communication, we introduce a novel type of visualization for combination datasets, named "Synergy Maps".
Four feature selection methods, mRMR, ReliefF, En-mRMR, and En-Relief, are used for performance comparisons on three publicly available datasets (two web image datasets named MIML [36] and NUS-WIDE-LITE [37], a remote sensing image dataset named USGS21 [38]).
We have considered two well-known and most challenging object datasets named as Caltech-101 [73] and Caltech-256 [11] to evaluate the object recognition performance of the proposed descriptor.
We describe a new exchange format for QSAR datasets, named QSAR-ML, which relies on the Blue Obelisk Descriptor Ontology for uniquely defining descriptors, and supports any implementation of these.
After stringent filtering of the retrieved data from literature and interaction databases, three disease datasets, named "HD dataset", "PD dataset", and "AD dataset", were generated.
Two datasets named Nlrp4_mouse (including 7 sequences) and Nlrp9_mouse (including 3 sequences) were investigated for different Site Models [ 73] implemented in PAML4.
This dataset consists of three sub datasets, named Wold data [ 16], Burge data [ 23], and Gimmond data [ 24] for short, which are originally utilized to validate the performance of the mseq method.
For the TCS gene sets available through P2CS, potentially 'mispredicted' TCSs were identified, and these were validated manually and added to the TCS datasets (named after the closest gene, and given an 'A' suffix).
Next, with the above preprocessed data of gene expression and SNP genotype, we converted the probe IDs (or SNP IDs) to gene symbols with gene (or SNP) annotations, producing two datasets named GeneExp data and GeneSNP data, respectively.
Thus, we generated four datasets named HD_Mix_45 (five replicates following the mixture data model with heritability 0.45), HD_Mix_10 (five replicates following the mixture data model with heritability 0.10), HD_One_45 (five replicates following the one normal data distribution with heritability 0.45) and HD_One_10 (five replicates following the one normal distribution with heritability 0.10).
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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