Sentence examples for using a classifier trained from inspiring English sources

However, when using a classifier trained on one GWAS to classify individuals from a different study, there is a risk that the background distribution of SNPs is very different between the populations in which the datasets have been collected, which could lead to errors, particularly when comparing diseases using datasets from different geographic origins.

Those in responsive regions were accurately predicted using a classifier trained on baseline brain network characteristics.

Using a classifier trained on annotated training data, the feature vectors extracted from the songs are assigned a genre.

Another early work by Popescul et al. [32] focused instead on link detection using a classifier trained on the feature vectors that describe the nodes of the graph.

This algorithm first screened the pea aphid genome for miRNA putative precursors (hairpin structure, see methods) using a classifier trained with a set of 30 validated pea aphid miRNAs.

To distinguish between ancestral alternative exons and other exons we used a classifier trained by Support Vector Machine (SVM), a machine learning algorithm [ 11].

Additionally, Prosigna is the only assay that is capable of classifying patients into one of the four breast cancer intrinsic subtypes using a classifier that was trained and verified to be consistent with the published PAM50 classifier.

A baseline method is tested using an SVM classifier trained only with the labeled target data.

The systems used a SVM classifier trained on a collection of tweets, which were labelled by two experts.

In a second approach, we used a linear classifier trained on rat and human phosphorylation data in set A. Eventually, both results were combined, taking into account their prediction performance as assessed within dataset A. A first prediction was based on the simple hypothesis that human phosphorylation levels (humP) and rat phosphorylation (ratP) should be similar under the same stimuli.

CPC uses an SVM classifier trained with respect to sequence similarity (using BLAST) and length (using FrameFinder).