ArticleBMC bioinformatics2018
Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 43 citations in OpenAlex.
- Dysregulation of SRSF11 in Cancer: Mechanistic Insights and Biomarker Potential for Diagnosis and Therapy.Journal of Cancer · 2026Review
- BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.Computational and structural biotechnology journal · 2024Article
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.Frontiers in genetics · 2023Article
- SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.Frontiers in genetics · 2022Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- Editorial: Machine Learning-Based Methods for RNA Data Analysis.Frontiers in genetics · 2022Article
- LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification.BioData mining · 2021Article
- LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification.BMC bioinformatics · 2021Article
- LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification.BMC bioinformatics · 2021Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.BMC bioinformatics · 2021Article
- Matrix factorization with neural network for predicting circRNA-RBP interactions.BMC bioinformatics · 2020Article
- GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network.Frontiers in genetics · 2020Article
- LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions.Frontiers in genetics · 2020Article
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.BMC bioinformatics · 2019Article
- Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.Frontiers in genetics · 2019Article
- Predicting biomedical relationships using the knowledge and graph embedding cascade model.PloS one · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIdentifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs.
resultsIn this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. AVAILABILITY: The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/ .
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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.