8 papers
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…
DeepPlantCRE: A Transformer-CNN Hybrid Framework for Plant Gene Expression Modeling and Cross-Species Generalization
Yingjun Wu, Jingyun Huang, Liang Ming +3
The investigation of plant transcriptional regulation constitutes a fundamental basis for crop breeding, where cis-regulatory elements (CREs), as the key factor determining gene ex…
Deep Learning and Explainable AI: New Pathways to Genetic Insights
Chenyu Wang, Chaoying Zuo, Zihan Su +4
Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an…
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…
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…
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…