ArticleScientific reports2021
lncRNA-disease association prediction based on latent factor model and projection.
Article in Scientific reports, 2021. 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.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique.Scientific reports · 2024Article
- Review
- lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.Scientific reports · 2022Article
- Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree.Scientific reports · 2022Review
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
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4 authors.
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Abstract
Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.
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