ArticleFrontiers in genetics2018
Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 39 citations in OpenAlex.
- Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation.BioData mining · 2025Review
- Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.Frontiers in endocrinology · 2024Article
- Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.Frontiers in big data · 2024Article
- Machine Learning Model in Predicting Sarcopenia in Crohn's Disease Based on Simple Clinical and Anthropometric Measures.International journal of environmental research and public health · 2022Article
- DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.Interdisciplinary sciences, computational life sciences · 2022Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke.Frontiers in aging neuroscience · 2022Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- Development of machine learning model for diagnostic disease prediction based on laboratory tests.Scientific reports · 2021Article
- EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.BMC bioinformatics · 2021Article
- MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.Journal of translational medicine · 2019Article
- Article
- Circulating non-coding RNAs in recurrent and metastatic ovarian cancer.Cancer drug resistance (Alhambra, Calif.) · 2019Review
- LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.