Which method to choose depends on study purpose – for example, if a certain phenotype is known to be an important factor in identifying the multi-gene signature, one should select supervised methodologies to make use of that information.
This paper presents a semi-supervised methodology for automatic recognition and classification of elderly activity in a cluttered real home environment.
This semi-supervised methodology allows us to build models based on weakly labelled data while incrementally learning latent positive and negative samples.
Consequently, to compare the predictive capability of our approach against other methods, we implemented prognostic models that differ in the way class-specific transcriptional association networks are inferred, and in the supervised classification methodology applied on the network information.
SKY supervised to prepare methodology from different techniques and prepared the discussion section.
In present paper, a supervised dimension reduction methodology named Active Subspace Method (Constantine, 2015) is introduced to deal with the high dimension problem of structural reliability.
Applications of supervised machine learning methodology continue to grow in the biomedical literature.
Using a supervised interaction inference methodology that exploits the experimentally identified interactions that are known so far, is likely to increase the true positive rate of our analysis when compared to unsupervised methods.
In this work, we propose a supervised machine-learning methodology to assess the accuracy of assigned molecular functions, based on simple topological properties of an organism's draft metabolic network.
A detailed description of the evaluation methodology, supervised learning algorithms used in classification and regression tasks as well as the quality metrics used to evaluate the performance of mCSM are available as Supplementary Material.
Various versions of the Minimum Redundancy Maximum Relevance approach have been described in references as a supervised variable selection methodology tailor-made for classification purposes, while its primary disadvantage has been explained as its high sensitivity to the presence of outlying measurements [ 15].
