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Following the pioneering study of Alizadeh et al (2000), which established a classifier for B-cell lymphomas, a number of data sets have been generated that contain expression signatures for various biologic and clinical tumour phenotypes.
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Methods: By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy.
By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy.
This approach established a REGS classifier capable of assigning each tumour sample an estimate of the probability of being resistant to each of the three drugs.
To gain insight into frequencies of sample annotation discrepancies in publicly available gene expression datasets, we established a male female classifier based on gene expression array data.
During the cross-validation process, we established an RVM classifier for each kind of activity, i.e., there were in total 13 different RVM classifiers for all kinds of activities.
The linear SVM classification algorithm producing a number of necessary weak classifiers is combined with Adaboost algorithm to establish a strong classifier.
Once you've established a person, use classifiers to talk about them.
Because the two obese groups could be differentiated as previously described, we employed several supervised machine learning algorithms to address whether this differentiation was sufficient to establish a robust classifier to identify responders and non-responders.
In both articles a variant of LDA was used to establish a REGS classifier neither of which resulted in predictions related to clinical outcome.
A gene expression centroid was constructed by averaging the expression values of the samples in primary and secondary angiosarcomas for each gene in order to establish a signature classifier for nearest centroid classification.
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