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The aim of this paper is to present a classifier based on a fuzzy inference system.
We present a classifier based on Convolutional Neural Network (CNN) to address this problem domain.
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To train a classifier based on phosphorylation (phos-based classifier), we regarded the potential cross talk sites as positive samples and the only phosphorylated sites as negative samples.
After feature extraction and selection, a classifier based on artificial neural network (ANN) is established.
We previously evaluated four alternatives: a multinomial naive Bayes classifier, a n-gram based classifier, a classifier based on grammatical inference techniques, and a classifier based on neural networks [46, 47].
Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning.
(A ) Classifier based on cilia siRNA screen features only.
We propose a classifier based on the solution of an ℓ1-minimization problem for classification.
Whole nuclei were segmented by a classifier based on the DAPI signal.
And then we built a classifier based on the selected features and applied the classifier to predict breast cancer from peripheral blood in an independent testing set.
Here we present CLARK (CLAssifier based on Reduced K-mers), a new tool that can accurately and efficiently classify objects to targets, based on reduced sets of k-mers (i.e., DNA words of length k).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com