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Validation of phenotypes across multiple cohorts of patients in various settings (see above) may result in the development of novel classifications of COPD patients, better reflecting their heterogeneity.
There are now numerous examples where gene expression profiles produce novel classifications of tissues in a manner that reflects differences in biological function or clinical outcomes[ 11, 12].
Perou et al use this technology to ask whether novel classifications in breast cancer can be identified from a series of 65 paired and non-paired breast specimens that included 36 ductal carcinomas and 2 lobular carcinomas [ 2].
In recent years, novel classifications based on the transcriptome analysis of breast cancer tissues have been established that revealed subtypes with distinct gene expression signatures and clinical outcome [ 3, 4].
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In this paper, we propose and evaluate a novel classification scheme for medical images.
On the basis of our analysis a novel classification of dislocations is proposed.
This paper presents a novel classification method, Deep Multitask Metric Learning (DMML), for offline signature verification.
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