Accurately predicting the secondary structure of RNA, particularly for long non-coding RNA, has direct implications in healthcare. However, many approaches are too costly in terms of computation budget to cope with the increasing complexity of long RNAs.
We propose DivideFold, an approach combining recursive cutting and machine learning techniques for predicting the secondary structures of long non-coding RNAs.
DivideFold : A Python repository to predict the secondary structure of long non-coding RNAs.