FRDNAC: A Future-Ready DNA Cryptography Paradigm for Secure Cloud Data Transmission Using Deep Learning-Enhanced Key Generation [0.03%]
基于深度学习增强密钥生成的未来导向型DNA加密范式用于安全的云数据传输
Nauman Umer,Miaolei Deng,Yuhong Zhang et al.
Nauman Umer et al.
The growing reliance upon cloud settings has rendered secure transmission of information essential. This study introduces the future-ready DNA-based cryptography (FRDNAC) paradigm, which combines DNA-based encryption with the feedback-assis...
Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering [0.03%]
用于增强单细胞多组学聚类的混合编码和自适应引导方法
Jing Li,Hong Wang,Jiafeng Yu et al.
Jing Li et al.
Cluster analysis is essential in single-cell multi-omics research, allowing for simultaneous analysis across different omics levels. It reveals cellular diversity and precisely differentiates cell types and their physiological states. Despi...
HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds [0.03%]
HP-MoleQ：一种有效的预测模型，用于高通量筛选来源于食品的保肝化合物
Qinyi Wang,Fangyuan Wang,Jiao Wang et al.
Qinyi Wang et al.
Hepatoprotective natural compounds are pivotal constituents for the formulation of functional foods and the prophylaxis of hepatic disorders. To enhance the efficiency and reliability of hepatoprotective compound discovery, we propose a new...
Pan-cancer Distant Metastasis Prediction Based on Graph Neural Network [0.03%]
基于图神经网络的泛癌种远处转移预测方法研究
Fengyun Zhang,Qiangguo Jin,Changming Sun et al.
Fengyun Zhang et al.
Distant metastasis (DM) is the primary driver of cancer-related mortality, and its clinical prediction remains challenging due to the lack of robust biomarkers. This study proposes a novel graph representation that effectively identifies di...
VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs [0.03%]
基于图的垂直联合学习的多组学数据集成方法(VFLING)
Xiaoli Li,Qi Li,Dedao Lu et al.
Xiaoli Li et al.
Modern machine learning models leveraging multi-omics data face significant privacy challenges due to the sensitive nature of patient information. Communication overhead and missing features in each institution can lead to a substantial dec...
MOFR: Multi-omics Feature Reconstruction for Cancer Classification and Metastasis Prediction [0.03%]
基于多组学特征重建的癌症分类和转移预测模型
Yun Tie,Dalong Zhang,Lei Shi et al.
Yun Tie et al.
A Bimodal Graph Neural Network with Transfer Learning and Contrastive Learning for Protein-Protein Interaction Site Prediction [0.03%]
基于迁移学习和对比学习的模态图神经网络蛋白质互作位点预测研究
Sheng Chang,Boyan Zhang,Changbo Li et al.
Sheng Chang et al.
Accurate prediction of protein-protein interaction sites (PPIS) is crucial for understanding molecular mechanisms and advancing precision medicine. However, due to the complexity of protein structure and function, as well as the sequential ...
GraphTransDTA: Drug-Target Affinity Prediction with Graph Transformer for Multimodal Data Fusion [0.03%]
基于图变换器的多模态数据融合药物靶点亲和力预测模型
Zihang Yuan,Fang Zheng,Yujin Ji et al.
Zihang Yuan et al.
Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots [0.03%]
基于单元格和点位相关性和重要性的单细胞空间图谱绘制
Juntao Li,Mengyuan Wang,Chenxi Xi et al.
Juntao Li et al.
Mapping cells to spatial locations is crucial for understanding biological processes, disease mechanisms, and therapeutic strategies. However, dropout events in both spatial transcriptomics and scRNA-seq data, along with intercellular inter...
DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction [0.03%]
基于解缠图注意力网络的知识图嵌入框架的药物相互作用预测(DDI)
Huimin Luo,Linfei Hou,Chaokun Yan et al.
Huimin Luo et al.
Combination therapy is an essential strategy for treating complex diseases. However, unintended drug-drug interactions (DDIs) can compromise therapeutic efficacy or even cause severe adverse reactions, posing significant challenges to clini...