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Protein-protein interface hot spots prediction based on a hybrid feature selection strategy.

Yanhua QiaoYi XiongHongyun GaoXiaolei ZhuPeng Chen
Published in: BMC bioinformatics (2018)
In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/ .
Keyphrases
  • protein protein
  • machine learning
  • deep learning
  • small molecule
  • neural network
  • squamous cell carcinoma