Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy

• 作者: Antai Ouyang Jinyong Cheng Ruoyu Zhang Wenpeng Lu Xiaodong Zhang Xu Zhang Yuehan Du
• 来源期刊: 量子计算杂志(英文)
• 学科: 工学
• 关键词: Multi-classifier COMBINATION ENTROPY PROTEIN SECONDARY structure prediction dynamic SELF-ADAPTATION
• 年，卷（期）: 2019,（1）
• 页码范围: 21-28
• 页数: 8页
• 分类号: TP1

摘要

The algorithm based on combination learning usually is superior to a singleclassification algorithm on the task of protein secondary structure prediction. However,the assignment of the weight of the base classifier usually lacks decision-makingevidence. In this paper, we propose a protein secondary structure prediction method withdynamic self-adaptation combination strategy based on entropy, where the weights areassigned according to the entropy of posterior probabilities outputted by base classifiers.The higher entropy value means a lower weight for the base classifier. The final structureprediction is decided by the weighted combination of posterior probabilities. Extensiveexperiments on CB513 dataset demonstrates that the proposed method outperforms theexisting methods, which can effectively improve the prediction performance.