论文标题
基于深钢筋学习的成对启发式序列对齐算法
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning
论文作者
论文摘要
已经开发了各种方法来分析生物体及其基因组序列之间的关联。其中，序列比对是最常用于比较生物基因组的比较。但是，传统的序列比对方法与序列的长度成比例相当复杂，将长序列（例如人类基因组）排列起来是显着挑战的。当前，可以使用多种多个序列比对算法，可以降低复杂性并改善各种基因组的比对性能。但是，改善成对比对算法的对齐性能的尝试相对较少。解决了这些问题之后，我们打算使用深入的增强学习提出一种新的序列对准方法。这项研究表明，深度强化学习到序列对准系统的应用方法以及深度强化学习如何改善常规序列比对方法的方式。
Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method.
