Among the machine learning-based methods, the ANNs and consensus methods were found among the top-performed methods.
The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method.
The existing methods for predicting residue residue contacts of α-helix proteins and TMH TMH interactions from the primary sequences can be generally divided into two categories: (1) machine learning-based methods, (2) statistical-based coevolution mining methods.
Like predictor-based statistical methods, machine learning-based methods lack the capability of detecting causal elements and tend to introduce many false positives, which may result in a huge cost for further biological validation experiments [ 18].
The datasets commonly used for the training and evaluation of such machine learning-based prediction methods are the Enzymes, Ion Channels, Nuclear Receptor, and G Protein-Coupled Receptor datasets [3].
Whereas the awareness of miRNA disease associations is growing, existing methods for identifying such associations falls in two broad categories: (i) text mining and curation of direct associations from literature and (ii) machine learning-based prediction methods.
Given the diagnostic accuracy achievable, machine learning-based categorization methods, such as the SVM technique, we have evaluated and now compared to radiological expertise, substantially extend the role of computers in clinical decision making (Ashburner et al., 2003).
The existing consensus methods combining several PSSM-based and machine learning-based methods showed generally improved performance than a single method.
We divide such techniques into two main categories: statistics-based methods and machine learning-based methods.
Among the applied standard statistical methods and the machine learning-based methods, RF effectively ranks causal SNPs to detect SNP interactions [ 13, 14].
These methods can be divided into three major categories: 1) sequence or structure conservation-based, 2) machine learning-based method, 3) and non-comparative methods.
