Experimental comparison of classification methods for key kinase identification for neurite elongation

Kinases in a developing neuron play important roles in elongating a neurite with their complex interactions. To elucidate the effect of each kinase on neurite elongation and regeneration from a small set of experiments, we applied machine learning methods to synthetic datasets based on a biologically feasible model. The result showed the ridged partial least squares (RPLS) algorithm performed better than other standard algorithms such as naive Bayes classifier, support vector machines and random forest classification. This suggests the effectiveness of dimension reduction done in RPLS.