Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

Abstract

The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells. Accurate identification of bacteriophage virion proteins is very important for understanding their functions and clarifying the lysis mechanism of bacterial cells. In this study, a new sequence-based method was developed to identify phage virion proteins. In the new method, the protein sequences were initially formulated by the g-gap dipeptide compositions. Subsequently, the analysis of variance (ANOVA) with incremental feature selection (IFS) was used to search for the optimal feature set. It was observed that, in jackknife cross-validation, the optimal feature set including 160 optimized features can produce the maximum accuracy of 85.02%. By performing feature analysis, we found that the correlation between two amino acids with one gap was more important than other correlations for phage virion protein prediction and that some of the 1-gap dipeptides were important and mainly contributed to the virion protein prediction. This analysis will provide novel insights into the function of phage virion proteins. On the basis of the proposed method, an online web-server, PVPred, was established and can be freely accessed from the website (http://lin.uestc.edu.cn/server/PVPred). We believe that the PVPred will become a powerful tool to study phage virion proteins and to guide the related experimental validations.

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

“…The feature selection method improves the performance by removing some redundant features in high-dimensional data [62–65]. In this study, we propose a two-step feature selection approach to select the most important features for predicting the phenotypic effects of SAVs.…”

Sequence-based predictive modeling to identify cancerlectins

“…The optimal feature subset will shorten the training and utilization times, reduce the measurement and storage requirements, avert overfitting and improve prediction performance [30]. Up to date, many effective feature selection techniques such as the analysis of variance [31], max-relevance-max-distance [32], minimum redundancy maximum relevance [33], principal component analysis [34] and recursive feature elimination algorithm [35, 36] have been proposed to reduce effects from noise or irrelevant features and provided good prediction results.…”

mGOF-loc: A novel ensemble learning method for human protein subcellular localization prediction

“…Jackknife test is widely used to evaluate the performance of predictors in protein subcellular localization and other field [65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82]. To make fair comparisons with existing methods, the jackknife test is also employed here.…”