With these feature vectors as input, standard machine learning methods such as Support Vector Machines (SVM), Naïve Bayes (NB) or Neural Networks (NN) can be used to predict the interaction of new drug target pairs.
In order to extend these studies, we have evaluated over 60 different machine learning methods, such as: support vector machines (with and without parameter optimization), naïve Bayesian, decision trees, random forest, meta-classifiers (boosting, bagging, grading) and many others.
Optimal Assignment Kernels follow a similar concept and have been used in machine learning methods such as Support Vector Machines [23]. Figure 4 Alignment of the atoms in molecules A and B based on the atom-to-atom similarity matrix.
Many traditional machine learning methods such as support vector machine and so on generally get biased classifier which leads to lower classification precision for minor class than major class.
These predictors utilize machine learning methods such as support vector machine and k-Nearest Neighbours classification.
Some machine learning methods, such as support vector machines (SVMs), have also been used to predict PPIs [ 19, 20].
Other machine learning methods, such as support vector machines, boosting and bagging classifiers could also be used to diagnose glaucoma.
Several computational methods have been developed to predict DNA- or RNA-binding residues in protein sequences using machine learning methods such as support vector machines (SVM) as classifiers.
Compared to other popular machine learning methods, such as support vector machines (SVM), training and tagging of CRF is relatively efficient with very high dimensional feature sets.
When the number of variables is large or when variables are highly correlated, these techniques can offer a better regression solution than classical regression methods and other machine learning methods such as support vector machine (SVM) [ 15].
