In order to analyze the impact of co-regulation of protein expression on biomarker identification, unsupervised PCA has been applied on the protein expression data (normalized on logarithmic scale), both from tumor and normal tissue samples.
In order to analyze the impact of co-regulation of protein expression on biomarker identification, unsupervised principal component analysis (PCA) has been applied on the protein expression data (normalized on logarithmic scale), both from tumor and normal tissue samples.
High prediction accuracy of major lineage classification with supervised tensor learning on multiple-biomarker tensors validates our unsupervised analysis of sublineages on multiple-biomarker tensors.
In the following section, we introduce multiple-biomarker tensors and present unsupervised and supervised learning experiments on multiple-biomarker tensors.
In addition to the single biomarker analysis and unsupervised clustering we also carried out classification of samples using miRNA expression patterns by applying Support Vector Machines SVMM, [ 24]) as implemented in the R e1071 package [ 31].
Supervised and unsupervised analyses were used to identify biomarkers explaining variance between groups defined by HIV status or drug abuse.
Next, we use the unsupervised tensor clustering framework on multiple-biomarker tensors to subdivide major lineages of MTBC into sublineages.
These innovations consist of novel flexible, active electrode arrays and unsupervised algorithms for detecting and classifying neurophysiologic biomarkers, specifically high frequency oscillations.
Based on the microarray data of 358 up-regulated transcripts (>2-fold change, P<0.01), breast cancer patients (n = 10) and matched controls (n = 10) could be classified into two distinct groups using unsupervised clustering, indicating the discriminatory power of salivary mRNA biomarkers (Figure S2).
The entries of the multiple-biomarker tensor which combines spoligotype and MIRU information can be formulated as: where and rik is the number of repeats in MIRU locus k of strain i. Multiple-biomarker tensors can be used for both unsupervised and supervised learning.
