ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics [0.03%]
基于主题模型和阻尼加权最小二乘法的单细胞与空间转录组数据统一流形分解方法 ST-LDAW
Xiaoyang Wang,Li C Xia,Huiling Liu et al.
Xiaoyang Wang et al.
Integrating single-cell RNA sequencing (scRNA-seq) with spatial transcriptomics (ST) enables the projection of cell-type-resolved transcriptional programs onto tissue architecture. However, existing integration methods are often unstable be...
MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation Site Prediction [0.03%]
一种基于多尺度动态卷积和增强位置感知变换器的DNA甲基化位点预测模型
An Gong,Yuyang Zhan,Lekai Zhang et al.
An Gong et al.
DNA methylation is a covalent modification of cytosine and adenine bases that regulates gene expression and underlies diverse biological processes and diseases. Existing computational methods often rely on fixed-scale feature extraction or ...
MVFAN-Kcr: A Multi-View Feature Fusion and Attention-Based Network for Lysine Crotonylation Site Identification [0.03%]
基于多视图特征融合和注意机制的赖氨酸巴豆酰化位点识别模型MVFAN-Kcr
Yun Zuo,Li Zhou,Wenjie Gong et al.
Yun Zuo et al.
Lysine crotonylation (Kcr), as an emerging post-translational modification, plays a crucial role in core life activities such as chromatin dynamics and gene expression. To address the current limitations of Kcr site detection techniques, in...
NphosNet: Predicting Protein N-Phosphorylation Sites via xLSTM and Enhanced PLM Features with a Weighted Three-Channel Cross-Attention Mechanism [0.03%]
NphosNet：通过带加权三通道交叉注意机制的xLSTM和增强的PLM特征预测蛋白质N-磷酸化位点
Lun Zhu,Yiyu Lin,Sen Yang
Lun Zhu
Protein phosphorylation, a pivotal post-translational modification mechanism, plays essential roles in cellular signaling and disease regulation. While O-phosphorylation has been extensively investigated, the biological significance of N-ph...
Attention-Guided Multi-View Contrastive Learning for Predicting Sparse Drug-Gene Associations [0.03%]
基于注意力引导的多视图对比学习的稀疏药物基因关联预测方法
Qingyong Wang,Yudong Liu,Shangping Zhao
Qingyong Wang
Employing deep learning techniques for drug discovery and repurposing necessitates the acceleration of predictions regarding drug-gene interactions. However, the scarcity of experimental support data often constrains the performance and gen...
ProtFormer-Site: Ultra-fast and Accurate Prediction of Protein-Protein Interaction Sites with Protein Language Model [0.03%]
ProtFormer-Site：使用蛋白质语言模型进行超快且准确的蛋白质-蛋白质相互作用位点预测
Lei Wang,Shali Dong,Han Zhang et al.
Lei Wang et al.
Identifying protein-protein interaction (PPI) sites is crucial for predicting protein function, uncovering disease mechanisms, and designing drugs. Experimental methods for PPI site identification are often costly and time-consuming, necess...
Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets [0.03%]
铜代谢对三阴性乳腺癌上皮间质转化的影响及治疗靶点的鉴定
Kai Zhuang,Lin Yan,Muhammad Waqas et al.
Kai Zhuang et al.
Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper metabolism has been implicated in TNBC progression, but its functional contributions remain i...
Enabling Drug-Drug Interaction Event Prediction with Multi-view-enhanced Chemical Structural Information [0.03%]
利用多视图增强的化学结构信息进行药物相互作用事件预测
Ge Jin,Junlin Xu,Hongxin Xiang et al.
Ge Jin et al.
Predicting drug-drug interaction (DDI) events is critical for ensuring patient safety, optimizing therapeutic efficacy, and advancing drug discovery. Deep learning-based models have recently attracted considerable attention in this domain a...
CDM-UNet: Content-Driven Enhanced Mamba Model for Medical Image Segmentation [0.03%]
基于内容驱动的医学图像分割玛博模型增强方法研究
Fan Zhang,Hui Chen,Binjie Wang et al.
Fan Zhang et al.
Segmenting medical images plays a pivotal role in diagnosis, treatment planning, and healthcare. Recent advancements in deep learning have significantly transformed this domain. Convolutional Neural Networks (CNNs) are proficient at extract...
BrainDEC: An M/EEG Unsupervised Representation Learning Framework with Disentangled Equivariance Constraint [0.03%]
具有解缠对称约束的M/EEG无监督表示学习框架BrainDEC
Xingyuan Song,Qiong Li,Haokun Mao et al.
Xingyuan Song et al.
Magnetoencephalography (MEG) and electroencephalography (EEG) are prominent non-invasive brain imaging techniques that have attracted considerable interest in neuroscience research. Unsupervised learning of M/EEG signals has become a critic...