The role of this page is a brief introduction to HBPred

Welcome to HBPred

    HBPred is a webserver for the identification of hormone-binding proteins (HBPs) based on machine learning method. In the predictor, dipeptide frequency were extracted to formulate samples. The analysis of variance (ANOVA)-based technique was proposed to rank 400 dipeptides. Subsequently, The incremental feature selection (IFS) was used to determine the optimal features which could produce the maximum accuracy. The support vector machine (SVM) was utilized to perform prediction. Jackknife cross-validated results showed that 88.6% HBP and 81.3% non-HBP can be correctly recognized, respectively, suggesting that our proposed model is very powerful. This study provides a new strategy to identify HBP. We hope the webserver could provide convenience for Biochemistry scholars.

LinDing Group

Figure Schematic diagram of hormone-binding protein