Publications by authors named "Keliang Cen"
circ2DGNN: circRNA-disease Association Prediction via Transformer-based Graph Neural Network.
IEEE/ACM Trans Comput Biol Bioinform
October 2024
Article Synopsis
- Investigating the links between circular RNAs (circRNAs) and diseases is essential for understanding disease mechanisms and developing better therapies, but existing methods only consider known data, missing vital interactions with other biomolecules.
- To overcome these limitations, researchers created circ2DGNN, a new computational model that uses a heterogeneous network to analyze the relationships between circRNAs, diseases, and other biomolecules more comprehensively.
- circ2DGNN employs advanced graph representation learning techniques, including a Transformer-like architecture, to improve predictions and has been shown to outperform current leading models in evaluations.
Correct prediction of potential miRNA-disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data.
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