Preference Integration and Optimization of Multistage Weighted Voting System Based on Ordinal Preference

Multistage voting is a common voting form through which the winners are selected. By virtue of weighted multistage voting rules, in this paper, we establish a weighted voting model by analyzing the correlation between individual preference and group preference. The weights of voters in each voting stage are adjusted through preference deviation degrees between individual preferences and group preference, and the ranking among candidates in each stage is determined according to weighted Borda function value. Examples are given to verify our model, which shows that weighted information aggregation model can mine more useful information from different individual preferences of voters to quicken the aggregation of group preference.

Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.