Hierarchical multi-label classification based on LSTM network and Bayesian decision theory for LncRNA function prediction

Growing evidence shows that long noncoding RNAs (lncRNAs) play an important role in cellular biological processes at multiple levels, such as gene imprinting, immune response, and genetic regulation, and are closely related to diseases because of their complex and precise control. However, most func...

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Bibliographic Details
Published in:Scientific reports 2022-04, Vol.12 (1), p.5819-5819, Article 5819
Main Authors: Feng, Shou, Li, Huiying, Qiao, Jiaqing
Format: Article
Language:English
Subjects:
631/114/1305
631/114/1314
631/114/2164
631/114/2403
Accuracy
Algorithms
Annotations
Bayes Theorem
Bayesian analysis
Classification
Computational Biology - methods
Computer applications
Decision Theory
Deep learning
Design
Gene Ontology
Genes
Genomes
Genomic imprinting
Humanities and Social Sciences
Immune response
Information technology
Long short-term memory
Mathematical models
multidisciplinary
Non-coding RNA
Ontology
Proteins
Research methodology
RNA, Long Noncoding - genetics
Science
Science (multidisciplinary)
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Summary:Growing evidence shows that long noncoding RNAs (lncRNAs) play an important role in cellular biological processes at multiple levels, such as gene imprinting, immune response, and genetic regulation, and are closely related to diseases because of their complex and precise control. However, most functions of lncRNAs remain undiscovered. Current computational methods for exploring lncRNA functions can avoid high-throughput experiments, but they usually focus on the construction of similarity networks and ignore the certain directed acyclic graph (DAG) formed by gene ontology annotations. In this paper, we view the function annotation work as a hierarchical multilabel classification problem and design a method HLSTMBD for classification with DAG-structured labels. With the help of a mathematical model based on Bayesian decision theory, the HLSTMBD algorithm is implemented with the long-short term memory network and a hierarchical constraint method DAGLabel. Compared with other state-of-the-art algorithms, the results on GOA-lncRNA datasets show that the proposed method can efficiently and accurately complete the label prediction work.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-09672-1