Conotoxins are disulfide-rich small peptides that are invaluable channel-targeted peptides and target neuronal receptors, which have been demonstrated to be potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate prediction of conotoxin superfamily would have many important applications towards the understanding of its biological and pharmacological functions. In this study, a novel method, named dHKNN, is developed to predict conotoxin superfamily. Firstly, we extract the protein's sequential features composed of physicochemical properties, evolutionary information, predicted secondary structures and amino acid composition. Secondly, we use the diffusion maps for dimensionality reduction, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions. Finally, an improved K-local hyperplane distance nearest neighbor subspace classifier method called dHKNN is proposed for predicting conotoxin superfamilies by considering the local density information in the diffusion space. The overall accuracy of 91.90% is obtained through the jackknife cross-validation test on a benchmark dataset, indicating the proposed dHKNN is promising.

These results indicate a possible use for the method in monitoring AML in peripheral blood by RT-PCR measurement of Indicator genes. In addition, the initial use of polyA PCR facilitates translation to very small clinical samples, including fractionated cell populations, of particular importance for monitoring haematological malignancy.

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Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans, which can be generally classified into disease related mutations and common ones. It has been generally accepted that SNPs caused amino acid substitutions are of particular interest as candidates for affecting susceptibility to complex diseases, such as cancer, which is a serious public issue affecting millions of people worldwide each year. In this study, we have developed an automated and robust method to distinguish cancer-related mutations from common polymorphisms from amino acid sequence, which has a significant meaning for the cancer diagnosis, prognosis and treatment. Multiple different sequential features are extracted and the most important features are finally selected for constructing the prediction model. Experimental results show that an overall 81.07% success rate has been obtained, indicating the proposed method is very promising in the clinical cancer research studies.