Sentence examples for approach trained on from inspiring English sources

Two baseline approaches of a 1-nearest neighbor classifier and a PCA approach trained on the source data are tested.

Our approach, trained on publicly available ERBB2IP-1 single-mutant phage display data, combined linear regression-based prediction for ligand positions whose specificity is determined by few PDZ positions, and single-mutant position weight matrix averaging for all other ligand columns.

The basis of our mirtron prediction method was the combination of an absolute count of mapping reads from Bowtie [ 47] and predicted secondary structures by UNAFold [ 50] scored using an SVM approach trained on fly mirtrons [ 51].

In this study, we have demonstrated that using a machine learning approach trained on the nucleosome landscape around genes, we were able to identify 231 putative genes, of which the majority showed evidence of expression in RT-PCR, EST, steady-state RNA-Seq, or polysomal RNA-Seq data sets in the malaria parasite, P. falciparum.

For now, fast Fourier transformation appears to be the most suitable for quantifying the EEG [ 28] but future algorithms may also incorporate ICU-specific EEG feature recognition and waveform analysis based on machine learning approaches trained on ICU EEG recordings.

We included that in our set of methods so that each approach is trained on exactly the same datasets.

In this paper we employed two independent classification methods for identifying the origin of psbA genes: 1) cuPSSM, based on position specific codon usage; and 2) a machine learning approach (SVM) trained on oligonucleotide (specifically tetranucleotide) composition.

But as the Olympic Games approach, training a spotlight on China's rights record, that view obscures a more complex reality: political change, however gradual and inconsistent, has made China a significantly more open place for average people than it was a generation ago.

The C 50 estimate is employed to select different acoustic models (back-end approach) which are trained on feature vectors appended to include the C 50 value (front-end approach).

We then describe a feature-based interface similarity measure by employing a supervised learning approach, which is trained on the known structures of protein-protein interfaces.

SIRENE by Mordelet and Vert [ 9] takes a different approach, with SVMs trained on feature vectors derived from single profiles.