ArticleBMC bioinformatics2019
ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions.
Article in BMC bioinformatics, 2019. 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.
- CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.Scientific reports · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction.Communications biology · 2020Article
- Matrix factorization with neural network for predicting circRNA-RBP interactions.BMC bioinformatics · 2020Article
- LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions.Frontiers in genetics · 2020Article
- Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.Frontiers in genetics · 2019Review
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5 authors.
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Abstract
backgroundLong non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.
resultsA five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.
conclusionsWith the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.
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