LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VG...

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Veröffentlicht in:Briefings in bioinformatics Jg. 25; H. 1
Hauptverfasser: Peng, Lihong, Huang, Liangliang, Su, Qiongli, Tian, Geng, Chen, Min, Han, Guosheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford Publishing Limited (England) 01.01.2024
Oxford University Press
Schlagworte:
Biological activity
Biological effects
Biomedical materials
Breast cancer
Colorectal cancer
Colorectal carcinoma
Decomposition
Disease
Kidneys
Lung cancer
Neoplasms
Non-coding RNA
Problem Solving Protocol
Singular value decomposition
Tumors
variational graph autoencoder
heterogeneous Newton boosting machine
lncRNA–disease association
singular value decomposition
ISSN:1467-5463, 1477-4054, 1477-4054
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Zusammenfassung:Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA–disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA–disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Lihong Peng and Liangliang Huang contributed equally to this work and share first authorship.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbad466