  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.
