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 improv...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 22; no. 4
Main Authors: Li, Ying, Zhang, Qi, Liu, Zhaoqian, Wang, Cankun, Han, Siyu, Ma, Qin, Du, Wei
Format: Journal Article
Language:English
Published: England Oxford University Press 01.07.2021
Oxford Publishing Limited (England)
Subjects:
Algorithms
Alignment
Artificial neural networks
Biological activity
Classification
Clustering
Computer applications
Ensemble learning
Gene sequencing
Machine learning
Neural networks
Non-coding RNA
Phylogenetics
Phylogeny
Representations
Source code
GcForest
ncRNAs clustering
deep fusion framework
multi-view structure feature representation
pairwise ncRNAs classification
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary: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 structural subunits, enabling us to capture the potential specificity between ncRNAs. For the classification of pairwise alignment of two ncRNA sequences, the F-value of GCFM improves 6% than an existing alignment-based method. Furthermore, the clustering of ncRNA families is carried out based on the classification matrix generated from GCFM. Results suggest better performance (with 20% accuracy improved) than existing ncRNA clustering methods (RNAclust, Ensembleclust and CNNclust). Additionally, we apply GCFM to construct a phylogenetic tree of ncRNA and predict the probability of interactions between RNAs. Most ncRNAs are located correctly in the phylogenetic tree, and the prediction accuracy of RNA interaction is 90.63%. A web server (http://bmbl.sdstate.edu/gcfm/) is developed to maximize its availability, and the source code and related data are available at the same URL.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbaa354