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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15764921 |
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Table of Contents:
- <p><span lang="EN">Long non-coding RNAs (lncRNAs) play diverse and critical roles in gene regulation, chromatin organization, and various cellular processes. Elucidating lncRNA-disease associations is essential for uncovering novel therapeutic targets and understanding disease mechanisms. In this study, we present PrediLnc, a comprehensive platform designed to disseminate predictive insights generated by our model GARNet (Graph convolution Attention RNA Network using stack ensemble to predict LncRNA-disease associations).</span></p> <p><span lang="EN">GARNet integrates multi-level biological information, including lncRNA sequence features, disease ontology terms, associative data at the gene, miRNA, and protein levels, to generate robust predictions. The model architecture leverages autoencoders for dimensionality reduction, graph convolutional networks and self-attention mechanisms for capturing topological and contextual node information, and a stacked ensemble learning framework for final prediction. The ensemble includes five base classifiers: Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron, with a Random Forest serving as the meta-classifier.</span></p> <p><span lang="EN">GARNet achieves strong performance in stratified cross-validation and is available as both a web server and a standalone tool through PrediLnc (</span><span lang="EN"><a href="https://dhanjal-lab.iiitd.edu.in/predilnc.html"><span>https://dhanjal-lab.iiitd.edu.in/predilnc.html</span></a></span><span lang="EN">). The platform's biological relevance has been validated through independent case studies, involving distinct lncRNAs and diseases, with predictions supported by evidence from over 500 PubMed studies, underscoring its reliability for the scientific community.</span></p> <p><strong><span lang="EN">Keywords: </span></strong><span lang="EN">long non-coding RNAs, lncRNA-disease association, disease ontology, deep learning, machine learning, GARNet, PrediLnc</span></p>