Ubiquitination is an important post-translational modification (PTM) process for the regulation
of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent
initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical
test approaches in conjunction with the application of computational methods. The identification of
ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility
of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that
computational methods are effective in extracting potential rules or inferences from biological sequence
collections. Up to the present, the computational strategy has been one of the critical research
approaches that have been applied for the identification of ubiquitination sites, and currently, there are
numerous state-of-the-art computational methods that have been developed from machine learning
and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets
is summarized, together with feature representation methods, feature selection approaches and
the classifiers involved in several previous publications. In an attempt to explore pertinent development
trends for the identification of ubiquitination sites, an independent test dataset was constructed
and the predicting results obtained from five prediction tools are reported here, together with some related
discussions.
Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation