6 citations · 12 across the 8 of their papers we have counts for
6 papers · 1 filter
DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops
Pengcheng Deng, Kening Liu, Mengxi Zhou +5
Genomic Selection (GS) uses whole-genome information to predict crop phenotypes and accelerate breeding. Traditional GS methods, however, struggle with prediction accuracy for comp…
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy
Ruizhan Xue, Huimin Deng, Fang He +2
With the extensive application of Graph Neural Networks (GNNs) across various domains, their trustworthiness has emerged as a focal point of research. Some existing studies have sh…
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Yiru Pan, Xingyu Ji, Jiaqi You +5
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulatio…
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
Xingyu Ji, Jiale Liu, Lu Li +2
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Netwo…
Mitigating Degree Bias in Signed Graph Neural Networks
Fang He, Jinhai Deng, Ruizhan Xue +2
Like Graph Neural Networks (GNNs), Signed Graph Neural Networks (SGNNs) are also up against fairness issues from source data and typical aggregation method. In this paper, we are p…
Self-Explainable Graph Transformer for Link Sign Prediction
Lu Li, Jiale Liu, Xingyu Ji +2
Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN…