论文标题
RL-MD：DNA图案发现的一种新颖的增强学习方法
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery
论文作者
论文摘要
从功能连接的未标记DNA序列集合中提取序列模式被称为DNA基序发现，它是计算生物学的关键任务。最近引入了几种基于深度学习的技术来解决此问题。但是，由于需要标记的数据，这些算法无法在现实情况下使用。在这里，我们介绍了RL-MD，这是一种基于新颖的增强学习方法，用于DNA图案发现任务。 RL-MD将未标记的数据作为输入，采用基于相对信息的方法来评估每个提出的主题，并利用这些连续的评估结果作为奖励。该实验表明，RL-MD可以识别现实世界中的高质量基序。
The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative information-based method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.
