CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN [0.03%]
基于条件变分自编码器和Wasserstein生成对抗网络的条件分子生成算法
Jianqiang Zheng,Ziqi Xu,Junwen Huang et al.
Jianqiang Zheng et al.
Molecular generation plays a vital role in advancing drug discovery, materials science, and chemical exploration. In this study, we integrated the conditional variational autoencoder (CVAE) with the Wasserstein generative adversarial networ...
Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus [0.03%]
金黄色葡萄球菌生物被膜形成的决定因素的整合全基因组关联研究及机器学习方法
Lydia R Sidarous,Mostafa S Ibrahim,Mohamed Elhadidy et al.
Lydia R Sidarous et al.
Staphylococcus aureus (S. aureus) is a well-recognized pathogen known for its multi-drug resistance and diverse virulence mechanisms. Its ability to grow biofilms on implanted medical devices enhances its antimicrobial resistance (AMR) and ...
Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation [0.03%]
基于扰动感知互学习和边缘感知不确定性损失的半监督医学图像分割以准确解剖划分
Waqas Anwaar,Van Manh,Wufeng Xue et al.
Waqas Anwaar et al.
Accurate segmentation of medical volumes in magnetic resonance imaging is essential for the exploration of organ structures. Despite the impressive performance of supervised learning in medical image segmentation, its reliance on the large ...
Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction [0.03%]
基于图的药物相互作用预测的多特征融合与交互模型
Xiaodan Wang,Hongjian Li,Jihong Wang
Xiaodan Wang
Drug-drug interactions (DDIs) can compromise therapeutic efficacy and patient safety, making accurate computational prediction highly important in drug discovery and clinical decision support. We propose Multi-GraphDDI, a structure-only fra...
GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction [0.03%]
GATESynergy：集成分子全局-局部聚合器和分层基因门控编码器的药物协同效应预测方法
Yan Wang,Jiana Ding,Zhiyao Han et al.
Yan Wang et al.
Combination drug therapy is an effective approach to combating drug resistance and enhancing therapeutic efficacy in complex diseases such as cancer. Nevertheless, discovering synergistic drug pairs remains difficult because of the enormous...
Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches [0.03%]
基于机器学习方法鉴定乳腺癌潜在的外泌体衍生mRNA生物标志物
Chenhao Li,Chunyan Wei,Qijia Tian et al.
Chenhao Li et al.
Introduction: Breast cancer remains a major global health burden, underscoring the urgent need for reliable early detection strategies. Exosomes, as mediators of intercellular communication, have shown promise in early tu...
Identifying RNA ac4C Modification Sites via Pseudo-Nucleotide Fingerprint Encoding and Multi-Scale Feature Integration [0.03%]
基于伪核苷酸指纹编码和多尺度特征整合的RNA.ac4C修饰位点识别方法
Yiming Wang,Fan Mo,Yun Sha et al.
Yiming Wang et al.
RNA modifications, particularly N4-acetylcytidine (ac4C), play essential roles in gene regulation by influencing mRNA stability, translational efficiency, and cellular responses to stress. Aberrant ac4C modifications have been implicated in...
Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion [0.03%]
基于双视图学习和多头自注意力机制融合的piRNA-疾病关联预测
Manyu Zheng,Ying Fang,Lijie Na et al.
Manyu Zheng et al.
PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining genomic stability and inhibiting transposable elements, and have been proven to participate i...
DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction [0.03%]
基于药物靶点亲和力预测的双交互及核多样性网络 DTANet+
Jin Xie,Junxiong Li,Yulong Wu et al.
Jin Xie et al.
Drug-target binding affinity (DTA) is central to computer-aided drug design. Although biochemical assays yield accurate measurements, their high cost and inefficiency limit scalability. Computational approaches, particularly deep learning, ...
STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder [0.03%]
基于邻居感知多视图屏蔽图形自编码器的时空转录组学数据空间域识别方法
Qi Gao,Junliang Shang,Shasha Yuan et al.
Qi Gao et al.
Recent advances in spatial transcriptomics (ST) have enabled the extraction of gene expression patterns while retaining spatial context. Identifying spatial domains is crucial for ST research. However, most existing spatial domain recogniti...