DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction [0.03%]
基于双视野注意力的多视图学习模型DSGCNLDA用于长链非编码RNA-疾病关联预测
Dengju Yao,Zhanhe Li,Xiaojuan Zhan et al.
Dengju Yao et al.
Long non-coding RNAs (lncRNAs) play key regulatory roles in biological activities, making it crucial to accurately predict lncRNA-disease relationships for understanding disease pathophysiology and developing effective prevention and treatm...
SSLCNV: A Semi-supervised Learning Framework for Accurate Copy Number Variation Detection [0.03%]
一种用于准确检测拷贝数变异的半监督学习框架(SSLCNV)
Ruchao Du,Jinxin Dong,Hua Jiang et al.
Ruchao Du et al.
Copy number variation (CNV) is a major type of structural variation (SV) that plays critical roles in genetic diversity and disease. Currently, many CNV detection tools have been developed. Although each tool exhibits different advantages u...
Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning [0.03%]
基于预训练语言模型和对比学习的蛋白质- DNA结合位点预测方法研究
Zhen Feng,Hui Yu,Xiaoya Guan et al.
Zhen Feng et al.
Top 10 Research Advances in Artificial Intelligence and Biomedical Science (2025) [0.03%]
人工智能与生物医学科学领域十大研究进展（2025）
Dong-Qing Wei
Dong-Qing Wei
iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction [0.03%]
基于Transformer的序列学习与DNA形状洞察整合的增强子预测方法(iEnhancer-Flow)
Huan Liu,Hanyu Luo,Lingyun Luo et al.
Huan Liu et al.
Purpose: Enhancers are critical non-coding regulatory elements, but their prediction remains challenging due to their variability and the absence of clear sequence motifs. This study aims to promote enhancer classificatio...
IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction [0.03%]
基于信息映射和融合的药物相互作用预测框架
Xiaoyang Li,Yuhao Zhang,Yafei Liu et al.
Xiaoyang Li et al.
Drug-drug interactions (DDIs) are crucial throughout various stages of drug development. Using computer-aided methods for accurate prediction of DDIs can enhance clinical safety and accelerate drug discovery. However, most existing deep lea...
Subtype-HM: A Novel Cancer Subtype Identification Method Based on Hypergraph Learning and Multi-omics Data [0.03%]
基于超图学习和多组学数据的新型癌症亚型识别方法subtype-hm
Jie Wang,Xin Huang,Hulin Kuang et al.
Jie Wang et al.
Cancer is a complex and lethal disease influenced by multiple factors, and accurate subtyping is crucial for personalized treatment and prognostic evaluation. Although deep learning has made progress in cancer subtype identification, existi...
HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network [0.03%]
基于多模态网络的下咽癌化疗敏感性分类方法HPCSMN
Weiqi Fu,Haiyan Li,Xiongwen Quan et al.
Weiqi Fu et al.
The treatment of hypopharyngeal cancer faces complex challenges, and accurate prediction of chemotherapy sensitivity is crucial for personalized treatment. In this study, a multimodal fusion network based on deep learning was used to classi...
MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction [0.03%]
基于双轴注意力的多阶矩外部图注意力网络蛋白质互作位点预测方法
Dangguo Shao,Yuyang Zou,Lei Ma et al.
Dangguo Shao et al.
Accurate prediction of protein-protein interaction (PPI) sites is fundamental to elucidating cellular mechanisms and advancing genomics. However, prevailing graph neural networks are constrained by two key limitations: they often neglect la...
GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction [0.03%]
GSF-DTA：一种创新的图序列融合框架用于药物-靶点亲和力预测
Guiyang Zhang,Yuemei Wang,Danni Zhao et al.
Guiyang Zhang et al.
Drug development is a lengthy and intricate process, where predicting drug-target affinity (DTA) is a vital step. Although traditional experimental techniques yield accurate and reliable results, their high cost and limited throughput rende...