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.
@article{arxiv.2209.15181,
title = {RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery},
author = {Wen Wang and Jianzong Wang and Shijing Si and Zhangcheng Huang and Jing Xiao},
journal= {arXiv preprint arXiv:2209.15181},
year = {2022}
}
Comments
This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytics