While the supervised framework was shown to enrich current PPI data with additional inferred PPIs, its applicability is still limited.
Ensembles of classifiers that utilize bagging, boosting, and hybrid-approaches for imbalanced datasets in the supervised framework were reviewed by Galar et al. [ 13].
A supervised framework was used to estimate the relative contribution of each type of external knowledge and from this a shortlist of promising regulators for each gene was predicted.
For unsupervised learning, k-means clustering, training and classification procedures for supervised and semi-supervised framework are implemented using the MATLAB® (Natick, MA).
The combination (Result 11) of RDEs with unigram features (Result 8), bigram features (Result 9) and supervised logistic regression (Result 3) improved the performance against the best individual ones, indicating the semi-supervised framework was able to incorporate rich feature set to enhance the performance.
This result suggests that using pre-existing knowledge of known compound protein interactions can improve the predictive performance of the method; thus, a supervised learning framework is encouraged for compound target prediction in practice.
The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article.
Such a supervised feature selection framework is highly demanded in making clinical decisions compared to the 'black box' predictive models generated by traditional machine learning algorithms.
In Yamanishi et al. (2008), a supervised learning framework was developed based on a bipartite graph, which integrates both chemical and genomic spaces by mapping them into a unified space.
In this paper, a framework is proposed for a supervised form of manifold learning called biased manifold embedding.
