Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network [0.03%]
基于异构网络多源特征融合的miRNA-药物相互作用预测方法
Chenyue Lei,Xiujuan Lei,Lian Liu et al.
Chenyue Lei et al.
Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions...
MPMB-DR: Meta-path Integration of Multi-source Biological Information for Drug Repositioning [0.03%]
基于多源生物信息的药物重定位的元路径集成方法MPMB-DR
Xiaoyan Sun,Zhenjie Hou,Wenguang Zhang et al.
Xiaoyan Sun et al.
Conventional approaches to drug discovery often require considerable time and effort. The promising solution is to repurpose existing drugs by identifying new therapeutic roles, thereby enhancing development efficiency. Drug repositioning b...
Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering [0.03%]
基于异构图随机注意神经网络和神经协同过滤的潜在微生物-疾病关联预测方法
Bo Wang,Wenlong Zhao,Xiaoxin Du et al.
Bo Wang et al.
Extensive research has underscored the intricate relationships between microbial communities and human diseases. Delving into these associations enhances our understanding of disease mechanisms and facilitates the development of novel thera...
Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising [0.03%]
基于拓扑结构保存和数据去噪的单细胞RNA序列的可解释多任务分析方法
Shengpeng Yu,Zihan Yang,Tianyu Liu et al.
Shengpeng Yu et al.
The advent of single-cell transcriptome sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the individual cell level, overcoming the limitations of bulk RNA sequencing. However, the explosive growth of scRNA...
Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images [0.03%]
融合细胞类型语义标注和整个切片图像的可解释癌症生存预测
Jinchao Chen,Pei Liu,Chen Chen et al.
Jinchao Chen et al.
Survival prediction involves multiple factors, such as histopathological image data and omics data, making it a typical multimodal task. In this work, we introduce semantic annotations for genes in different cell types based on cell biology...
hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks [0.03%]
基于多特征融合和图神经网络的可解释深度学习心脏毒性预测模型(hERG-MFFGNN)
Bingyu Jin,Jiarun Wang,Xin Yang et al.
Bingyu Jin et al.
Drug-related cardiotoxicity, most notably arrhythmia, represents a major challenge in drug development. Inhibition of hERG potassium channel by certain compounds has the potential to delay cardiac repolarization, manifested as QT interval p...
MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics [0.03%]
基于多核学习与网络脉冲动力学的黑色素瘤致病基因识别方法研究
Linconghua Wang,Ju Xiang,Zihao Guo et al.
Linconghua Wang et al.
Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatment strategies. Network-based approaches effectively capture the synergistic interactions am...
Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction [0.03%]
因果变压器，从结构化医疗记录中学习嵌入；以及复杂疾病风险预测的多源数据集成方法
Zeming Li,Yu Xu,Debajyoti Chowdhury et al.
Zeming Li et al.
Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches ...
ORMCKB: A Knowledge Database for Personalized Medicine in Deciphering the Oral Microbiome-Disease Axis [0.03%]
ORMCKB：一个用于精准医学解析口腔微生物与疾病关系的知识库
Yutao Wu,Yi Zhou,Wenjing Shi et al.
Yutao Wu et al.
The oral microbiome plays a crucial role in the development and progression of diseases. The complex interactions between the oral microbiome and diseases are challenging for clinicians in clinical decision-making and scientific research. T...
Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell [0.03%]
基于HPCell精确预测空间组织学图像中的细胞类型丰度
Yongkang Zhao,Youyang Li,Weijiang Yu et al.
Yongkang Zhao et al.
Recent advancements in spatial transcriptomics (ST) have revolutionized our ability to simultaneously profile gene expression, spatial location, and tissue morphology, enabling the precise mapping of cell types and signaling pathways within...