计算机工程与应用2019，Vol.55Issue(12)：132-139,8.DOI:10.3778/j.issn.1002-8331.1811-0059
基于强化学习的HP模型优化方法研究
Research on HP Model Optimization Method Based on Reinforcement Learning
摘要
Abstract
Protein structure prediction is an important factor in the area of bioinformatics. Predicting the two-dimensional structure of proteins based on the Hydrophobic Polarity model(HP model)is a typical Non-deterministic Polynomial(NP)-hard problem. Currently, HP model optimization methods include the greedy algorithm, particle swarm optimization, genetic algorithm, ant colony algorithm and the Monte-Carlo simulation method. However, the robustness of these meth-ods are not sufficient, and it is easy to fall into a local optimum. Therefore, a HP model optimization method, based on rein-forcement learning is proposed. In the full state space, a reward function based on energy function is designed and a rigid overlap detection rule is introduced. By using the characteristics of the continuous Markov optimal decision and maximizing global cumulative return, the global evolutionary relationship in biological sequences is fully exploited, and effective and stable predictions are retrieved. Eight classical sequences from publications and Uniref50 are selected as experimental objects. The robustness, convergence and running time are compared with the greedy algorithm and particle swarm optimi-zation algorithm, respectively. Both reinforcement method and swarm optimization method can find all the lowest energy structures for these eight sequences, while the greedy algorithm only detects 62.5%. Compared with particle swarm opti-mization, the running time of the reinforcement method is 63.9% lower than that of particle swarm optimization.关键词
强化学习/HP模型/结构预测Key words
reinforcement learning/ Hydrophobic Polarity(HP)model/ structure prediction分类
信息技术与安全科学引用本文复制引用
吴宏杰,杨茹,傅启明,陈建平,陆卫忠..基于强化学习的HP模型优化方法研究[J].计算机工程与应用,2019,55(12):132-139,8.基金项目
国家自然科学基金(No.61772357,No.61672371,No.61502329,No.61876217) （No.61772357,No.61672371,No.61502329,No.61876217）
江苏省"333工程"科研项目,六大人才高峰项目(No.DZXX-010) （No.DZXX-010）
苏州市科技项目(No.SNG201610,No.SYG201704) （No.SNG201610,No.SYG201704）
苏州科技大学江苏省建筑智慧节能重点实验室项目. （）
