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For each subset, we construct a classifier using the data in k − 1 subsets and then evaluate the classifier's performance on the data in the k-th subset.
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To test this, we constructed a classifier using the Elastic Net method to classify individuals as fasted or fed based on the gene expression traits (38).
Using 38 pea aphid miRNAs already found by homology search as a learning set, we constructed a classifier using all these features.
We first constructed a classifier using the three biochemical features, including the hydrophobicity index (H), side-chain pKa value (K), and molecular weight (M) of an amino acid.
To discriminate between the fasted and fed states, we constructed a classifier using the gene expression data and the Elastic Net method (38).
In searching for discriminative pathways using IFR, we found that we could construct a perfect classifier using the genes identified with B Cell Maturation and Activation (BCell) combined with those identified as relating to Cell Adhesion Molecules (CAM).
Essentially, it constructs a binary classifier using labeled training samples.
Next, to assess if gene body acetylation can distinguish genic from nongenic regions, we constructed a supervised classifier using only histone acetylations.
We employed an SVM-based approach to construct a classifier that can be used to predict the class membership as being MSMG or not-MSMG for genes by combining genome-wide mRNA expression profiling data and ChIP-seq data.
Although this probeset could be used to construct a classifier with a reasonably large margin, the presence of a sample of normal tissue with expression close to the cancer group drastically reduces the specificity of a classifier based upon this probeset.
In the third step, temperature data were then used to construct a classifier for detecting the trends.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com