RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery

The DNA sequences that contain the same motif, a specific sequence pattern, are often bound by a particular transcriptional factor (TF) or TF combination. Biologists have shown that TF are crucial in biological processes such as alternative splicing [1], RNA degradation [2], and transcriptional regulation [3]. Different types of cells express unique combinations of TFs, which might be viewed as the basic mechanism for cell differentiation [4]. Identifying the motif from a collection of unlabeled DNA sequences that hold common regulatory or functional characteristics is a essential task for computational biology known as DNA motif discovery (DMD) (Figure 1). The input for the DMD is thousands of sequences, each containing hundreds of nucleotides. Unknown portions of these sequences contain the motif, while the remaining sequences do not.