Sentence examples for supervised techniques such from inspiring English sources

It can be used in combination with other supervised techniques such as recursive partitioning [ 32], frequently used in DRG systems [ 40], to explain costs of care.

If the experimental conditions are known, supervised techniques such as support vector machines are suitable to extract the dependencies between conditions and gene expression or to identify condition-indicative genes.

Due to the time-consuming and mistake-prone nature of manual annotation, semi-supervised techniques such as bootstrapping appeared as an alternative.

Although we will not discuss this aspect explicitly here, molecular representations can also be used to directly predict properties using supervised learning techniques such as kernel-ridge regression or neural networks.

Other bioinformatics' strategies include supervised learning techniques, such as artificial neural networks (ANN) or support vector machines (SVM), in order to evaluate easily and reliably a great amount of complex data [12], [13].

A number of subgrouping studies have used supervised analysis techniques, such as logistic regression, in forms that can only model two subgroups.

Unsupervised and supervised classification techniques such as PCA and Hierarchical Clustering were used to distingue different tissues (tumoral and peritumoral) from the same patient basing on a restricted signature of these miRs.

Known true positive and true negative relationships allow researchers to calculation power and sensitivity, and even provide the possibility of applying supervised learning techniques such as support vector machines or random forest classifiers.

In this phase, supervised data mining techniques such as clustering, classification, and association can be employed for feature selection and predictive modeling.

Recently, computational methods for assessing the reliability of putative protein interaction have also been studied based on supervised machine learning techniques, such as Bayesian network [31, 32], and maximum likelihood estimation [25].

When the same atom is identified for multiple motifs, supervised machine learning techniques such as K-nearest neighbors are used to remove duplicates and assign the atom to the correct motif.