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This characterization was complemented by the analysis of a dataset of samples newly collected, from two regions within the Pleistocene area, where the sedimentary facies of samples was additionally distinguished.
To identify the binding media, the relative percentage content of amino acids in each sample was compared to those from a dataset of samples of egg, casein and animal glue.
Fig. 2 Main steps of our proposed method given a dataset of samples: 1 roughly cluster in order to swiftly make groups of similar sound samples. 2 Find the major cluster i.e. the cluster best representative of the tested structure.
Since hammering is usually conducted locally, around a suspected area on the structure's surface, the dataset of samples is not expected to grow up to very large sizes and therefore it can be considered that, in the scope of our application, it is satisfying.
Particularly, in comparison with the SVC, the SVR has excellent regression abilities to infer the property values from a limited dataset of samples and it is especially effective when the input data is not linearly separable and the kernel function is required to map the data into a higher dimensional space to find the optimal separating hyperplane.
Obviously, the above analysis routine can be applied on any other dataset of samples with different kinds of complex diseases.
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One focus of research in graphical models is how to learn them from a dataset of sample cases.
We first assess the performance of our method using a simulated dataset of sample size m=50.
The paired-end sequencing dataset of sample B was split in two individual datasets arbitrarily designated B-left and B-right, corresponding to the forward and reverse Illumina reads, respectively.
In this way, the dataset of sampled EC-genes were constructed with n = 3,729 + 5,963* S and had the same proportion of genes with paralogs as that of the non-EC-genes.
For each tumor type, we selected the largest datasets of samples for colon adenocarcinoma (COAD) produced by the Baylor College of Medicine and lung and prostate adenocarcinomas (LUAD and PRAD, respectively) produced by the Broad Institute of MIT and Harvard.
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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