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GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions
Jing Yan1, Wenyan Qu1, Xiaoyi Li1
1Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Computational Biology and Chemistry
|December 9, 2023
Summary
This study introduces GATLGEMF, a novel computational model for predicting non-coding RNA-protein interactions (NPIs). GATLGEMF utilizes graph neural networks and attention mechanisms to achieve superior performance in identifying these crucial biological links.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Non-coding RNAs (ncRNAs) are crucial regulators of fundamental biological processes.
- Dysregulation of ncRNAs is linked to various complex human diseases.
- Understanding ncRNA-protein interactions (NPIs) is vital for elucidating ncRNA function and disease mechanisms.
Purpose of the Study:
- To develop an efficient and accurate computational model for predicting NPIs.
- To leverage graph neural networks (GNNs) for feature extraction from complex biological networks.
- To improve the identification of novel NPIs for deeper biological insights.
Main Methods:
- Proposed a novel computational model named GATLGEMF.
- Employed a line graph transformation strategy to optimize feature representation.
- Utilized an attention network integrated with GNNs for NPI prediction.
- Validated the model on four benchmark datasets.
Main Results:
- GATLGEMF demonstrated superior performance compared to existing methods.
- Achieved high Area Under the Curve (AUC) values: 92.41% on RPI2241 and 98.93% on NPInter v2.0.
- A case study confirmed the model's capability in predicting novel NPIs from known interactions.
Conclusions:
- GATLGEMF is an effective computational tool for predicting NPIs.
- The model offers a low-cost, high-efficiency approach to advance NPI research.
- Findings contribute to understanding ncRNA roles in biological processes and diseases.
Keywords:
Graph attention networksGraph topology featuresLine graphsNcRNA-protein interactionsNode featuresMore Related Videos
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