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We developed a classifier for these subtypes and validated it in 70 tumors from a different population.
By utilizing these novel features, we developed a classifier for ncRNA gene prediction.
We have developed a classifier for ranking protein sequences according to their toxin-like properties.
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One study, for example, developed a classifier of 48 miRNAs from a sample of 336 primary and metastatic tumours, and was able to use this classifier to accurately predict the tissue origin in 86% of a blind test set, including 77% of the metastatic tumours (Rosenfeld et al, 2008).
A similar approach was used to develop a classifier for histologic grade.
The PAM program was used to develop a classifier for the benign, borderline and malignant phenotypes (Tibshirani et al, 2002).
A unique approach for developing a classifier for categorizing voluntary coughs was used that was based on the subspace projection of the principal components into a vector space.
For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG) using support vector machines (SVM).
Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known.
In this work, we focus on the comparison approach, not on developing a classifier specially suited for the particular task of GWAS classification.
A self-learning approach is developed to train a classifier for radar image interpretation and autonomous navigation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com