Sentence examples for using an unsupervised cluster from inspiring English sources

In a pilot study of acute meningitis, CSF spectra from small numbers of controls, patients with viral meningitis and bacterial/fungal meningitis were distinguished using an unsupervised cluster analysis method [8], suggesting that classification according to etiology is possible using larger data sets.

This was confirmed using an unsupervised cluster analysis: the expression profiles at the same time points from different regions were similar to one another and therefore clustered together compared to those from neighboring time points (Additional file 1 figure S3).

A library of overlapping local structural prototypes was built using an unsupervised clustering approach called "hybrid protein model" (HPM).

In Stage 1, a set of clusters is identified using an unsupervised clustering method which is simultaneously applied to the training feature vectors extracted from the different classes.

First, all the spatial direction vectors d ( t, f ) = S x ( t, f ) S x ( t, f ), with (t f) ∈ Ω, are clustered by using an unsupervised clustering algorithm and taking into account that the number of sources is supposed to be known.

A heat map of over 6,000 differentially regulated genes was generated using an unsupervised clustering algorithm.

Consensus clustering, using an unsupervised clustering algorithm [ 17], was applied to the gene expression dataset to identify patients with similar overall expression profiles.

Using an unsupervised clustering algorithm, we identified probes with similar levels of differential DNA methylation in the CIMP+ and CIMP− samples across multiple tissues (see Additional file 2).

Clustering animals based on the genetic relationship matrix clearly demonstrates this division between cattle populations, which is also seen using an unsupervised clustering with selected number of clusters K = 2 (Additional file 1: Figure S 1A).

The expression patterns of these 6384 probe sets across groups were visualised using an unsupervised clustering algorithm, which assigns samples to clusters (nodes) based on similarity of transcriptional pattern.

By using an unsupervised clustering approach on the set of pathogenic proteins without the use of heuristic thresholds, we can predict putative effectors which would have gone undetected with pipeline approaches that rely on heuristic assumptions.