ArticleScientific reports2023
Computational prediction of disease related lncRNAs using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- PlantLncBoost: key features for plant lncRNA identification and significant improvement in accuracy and generalization.The New phytologist · 2025Article
- Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.Scientific reports · 2025Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- The lncRNAs PART1 and ADAMTS9-AS2 act in an antithetic manner on AR signaling and induction of cellular senescence in prostate cancer cells.International journal of surgery (London, England) · 2025Article
- Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Article
- An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions.Bioinformatics advances · 2025Review
- Targeting and engineering long non-coding RNAs for cancer therapy.Nature reviews. Genetics · 2024Review
- Targeting epigenetic deregulations for the management of esophageal carcinoma: recent advances and emerging approaches.Cell biology and toxicology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNAs (lncRNAs), which were once considered as transcriptional noise, are now in the limelight of current research. LncRNAs play a major role in regulating various biological processes such as imprinting, cell differentiation, and splicing. The mutations of lncRNAs are involved in various complex diseases. Identifying lncRNA-disease associations has gained a lot of attention as predicting it efficiently will lead towards better disease treatment. In this study, we have developed a machine learning model that predicts disease-related lncRNAs by combining sequence and structure-based features. The features were trained on SVM and Random Forest classifiers. We have compared our method with the state-of-the-art and obtained the highest F1 score of 76% on SVM classifier. Moreover, this study has overcome two serious limitations of the reported method which are lack of redundancy checking and implementation of oversampling for balancing the positive and negative class. Our method has achieved improved performance among machine learning models reported for lncRNA-disease associations. Combining multiple features together specifically lncRNAs sequence mutation has a significant contribution to the disease related lncRNA prediction.
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Registered trials
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