Abstract
Dynamic and reversible N6-methyladenosine (m6A) modifications are associated with many essential cellular functions as well as physiological and pathological phenomena. In-depth study of m6A co-functional patterns in epi-transcriptomic data may help to understand its complex regulatory mechanisms. In this chapter, we describe several biclustering mining algorithms for epi-transcriptomic data to discover potential co-functional patterns. The concepts and computational methods discussed in this chapter will be particularly useful for researchers working in related fields. We also aim to introduce new deep learning techniques into the field of co-functional analysis of epi-transcriptomic data.
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