Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.

ABSTRACT: Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs' functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs' functions, more and more computational methods have been introduced to improve the classification automatically and accurately. In this paper, based on a convolutional neural network and a deep forest algorithm, multi-grained cascade forest (GcForest), we propose a novel deep fusion learning framework, GcForest fusion method (GCFM), to classify alignments of ncRNA sequences for accurate clustering of ncRNAs. GCFM integrates a multi-view structure feature representation including sequence-structure alignment encoding, structure image representation and shape alignment encoding of

SUBMITTER: Li Y 

PROVIDER: S-EPMC8294561 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature