ArticlePloS one2023
lncRNA-disease association prediction based on the weight matrix and projection score.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- Neighborhood based computational approaches for the prediction of lncRNA-disease associations.BMC bioinformatics · 2024Article
- Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism.Scientific reports · 2024Article
- Unveiling the role of long non-coding RNA MALAT1: a comprehensive review on myocardial infarction.Frontiers in cardiovascular medicine · 2024Review
- CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model.Plants (Basel, Switzerland) · 2023Article
- GBDTLRL2D Predicts LncRNA-Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network.Frontiers in cell and developmental biology · 2021Review
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5 authors.
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Abstract
With the development of medical science, long noncoding RNA (lncRNA), originally considered as a noise gene, has been found to participate in a variety of biological activities. Several recent studies have shown the involvement of lncRNA in various human diseases, such as gastric cancer, prostate cancer, lung cancer, and so forth. However, obtaining lncRNA-disease relationship only through biological experiments not only costs manpower and material resources but also gains little. Therefore, developing effective computational models for predicting lncRNA-disease association relationship is extremely important. This study aimed to propose an lncRNA-disease association prediction model based on the weight matrix and projection score (LDAP-WMPS). The model used the relatively perfect lncRNA-miRNA relationship data and miRNA-disease relationship data to predict the lncRNA-disease relationship. The integrated lncRNA similarity matrix and the integrated disease similarity matrix were established by fusing various methods to calculate the similarity between lncRNA and disease. This study improved the existing weight algorithm, applied it to the lncRNA-miRNA-disease triple network, and thus proposed a new lncRNA-disease weight matrix calculation method. Combined with the improved projection algorithm, the lncRNA-miRNA relationship and miRNA-disease relationship were used to predict the lncRNA-disease relationship. The simulation results showed that under the Leave-One-Out-Cross-Validation framework, the area under the receiver operating characteristic curve of LDAP-WMPS could reach 0.8822, which was better than the latest result. Taking adenocarcinoma and colorectal cancer as examples, the LDAP-WMPS model was found to effectively infer the lncRNA-disease relationship. The simulation results showed good prediction performance of the LDAP-WMPS model, which was an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease relationship data.
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