HGKAN: Hypergraph Kolmogorov-Arnold Networks for Interpretable Prediction of Herb-Symptom Associations [0.03%]
HGKAN：用于解释草药症状关联预测的超图科莫哥罗夫-阿诺德网络
Xudong Liang,Yamei Huang,Tao Lin
Xudong Liang
With the increasing availability of large-scale traditional Chinese medicine (TCM) data, accurate prediction of herb-symptom associations (HSAs) has become a crucial task in natural drug discovery. Existing computational approaches mainly e...
HiHPO: Multimodal Hierarchical Graph Learning for Predicting Missing Protein-Phenotype Associations [0.03%]
基于多模态层次图学习的预测缺失蛋白质表型关联的方法
Hancheng Liu,Weiqi Zhai,Shaojun Wang et al.
Hancheng Liu et al.
Understanding protein-phenotype associations is essential for elucidating disease mechanisms and supporting phenotype-driven diagnosis. Although the Human Phenotype Ontology (HPO) provides a standardized framework for phenotypic description...
CMAF-DDI: A Knowledge-Enhanced Cross-Modal Fusion Method Leveraging Protein Representation for Multi-Class Drug-Drug Interactions [0.03%]
一种利用蛋白质表征的知识增强型跨模态融合方法用于多类药物相互作用(CMAF-DDI)
Hengpeng Zhao,Xiaoli Lin,Jun Pang et al.
Hengpeng Zhao et al.
Accurate prediction of drug-drug interactions (DDIs) is crucial for medication safety and personalized treatment. Most existing methods primarily exploit molecular graphs or biomedical knowledge graphs, while target protein sequence informa...
TrustEndo: A Conformal-Calibrated MLLM With Retrieval-Augmented Reasoning for Trustworthy Gastroscopic Diagnosis [0.03%]
TrustEndo：一种用于可信胃镜诊断的校准型多模态语言和视觉大型预训练模型，具有检索增强型推理能力
Yu Ma,MingLiang Feng,Honghu Wang et al.
Yu Ma et al.
Multimodal Large Language Models (MLLMs) can generate natural language diagnostic descriptions from gastroscopic images, but their clinical use is blocked by two problems: hallucination of plausible-yet-wrong claims, and the lack of statist...
Speaking the Native Language of LLMs: A Discrete Architecture for Molecular Comprehension [0.03%]
LLM的母语说话：分子理解的离散架构
Haoyang Liu,Xikang Feng,Fei Guo et al.
Haoyang Liu et al.
Large Language Models (LLMs) have emerged as a powerful paradigm for scientific discovery, yet adapting them to natively comprehend complex molecular structures remains a fundamental challenge. To capture structural nuances, the community h...
Game Theory-Based Adaptive Human-Machine Joint Learning for Online MI-BCI Decoding [0.03%]
基于博弈论的自适应人机联合学习在线MI-BCI解码方法
Yitao Jing,Jiaxing Wang,Xinlin Que et al.
Yitao Jing et al.
Motor imagery based brain-computer interface (MI-BCI) has been extensively researched for neurorehabilitation and motor assistance, while the performance of previous MI-BCI systems for online decoding motor intentions is still not satisfact...
GSSCMI: Efficient Co-Attention and Multimodal Contrastive Learning for Enhanced circRNA-miRNA Interaction Prediction [0.03%]
基于高效共注意力和多模态对比学习的circRNA-miRNA互作增强预测模型
Lihao Sun,Xin Wang,Fang Wang et al.
Lihao Sun et al.
Existing circRNA-miRNA interaction prediction methods have not fully exploited the spatial folding information in circRNAs and pre-miRNAs. Furthermore, current methods inadequately address intramolecular modal consistency and intermolecular...
MSIGR-PLA: Integrating Multi-Scale Interaction and Global Representations for Protein-Ligand Affinity Prediction [0.03%]
基于多尺度交互和全局表示的蛋白质-配体亲和力预测模型
Hangchen Zhang,Haoran Chen,Chang Liu et al.
Hangchen Zhang et al.
Accurate prediction of protein-ligand affinity (PLA) is crucial for accelerating drug discovery. Current methods exhibit limitations in extracting local protein-ligand interaction features and global representations, thereby hindering predi...
SAGE: Subject-Adaptive Graph-Based Modeling With Decision-Level Calibration for Stress and Cognitive Workload Monitoring [0.03%]
自适应主体的图模型决策级校准用于压力和认知负荷监测
Chenpei Xie,Yiting Wei,Mostafa Haghi et al.
Chenpei Xie et al.
In real-world driving scenarios, reliable monitoring of drivers' stress and cognitive workload is critical for driving safety. Both tasks can be characterized using multimodal physiological signals; however, due to pronounced inter-subject ...
Efficient Medical Segmentation Anything Model for Robust Lightweight Segmentation under Imperfect Data in Embedded Healthcare [0.03%]
一种高效的医疗分割Anything模型，在嵌入式医疗保健不完善数据下进行稳健的轻量级分割
Chin-Feng Lai,Shih-Yeh Chen,Po-Chih Liu et al.
Chin-Feng Lai et al.
Healthcare Industry 5.0 is accelerating the deployment of medical imaging intelligence on embedded and edge devices, where medical data are often imperfect due to acquisition noise, low-contrast regions, and boundary ambiguity, while comput...