Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction [0.03%]
自适应图提示与对比学习相结合：代谢物疾病关联预测的多视图框架
Xiaoxin Du,Xue Yang,Bo Wang et al.
Xiaoxin Du et al.
Metabolite-disease associations (MDAs) are critical for advancing precision medicine, yet existing computational methods face challenges in data sparsity, noise robustness, and feature representation. We propose GPLCL (graph prompt-enhanced...
AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease [0.03%]
阿尔茨海默病的早期检测与诊断：从2D-CNN到3D-CNN
Romoke Grace Akindele,Samuel Adebayo,Ming Yu et al.
Romoke Grace Akindele et al.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the ageing population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybr...
VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder [0.03%]
基于异构图变自动编码器的药物-靶点相互作用预测模型
Chen Zhang,Jiaqi Sun,Linlin Xing et al.
Chen Zhang et al.
Identifying drug-target interaction (DTI) is crucial for drug discovery and repositioning. Biological identification of DTI is costly and time-consuming, so heterogeneous network methods for DTI prediction, which can speed up drug developme...
A Novel Dual-Level Momentum Distillation Method with Extreme Thresholding for Imputing Single-Cell RNA Sequencing Data [0.03%]
具有极端阈值的新型双层动量蒸馏法在单细胞RNA测序数据插补中的应用
Binhua Tang,Xinyu Gao,Guowei Cheng
Binhua Tang
Single-cell RNA sequencing (scRNA-seq) plays a vital role in studying cellular heterogeneity and gene expression patterns. However, the sequencing dropout phenomena still pose a significant challenge. Genes with low expression levels may be...
AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides [0.03%]
基于Transformer的方法整合LSTM和注意机制预测抗炎肽
Shengli Zhang,Jingyi Ren
Shengli Zhang
Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framew...
Relevance of 3D Rotationally Equivariant Neural Networks for Predicting Protein-Ligand Binding Affinities [0.03%]
旋转等变神经网络在预测蛋白质-配体结合亲和力中的应用研究
Gaili Li,Yongna Yuan,Ruisheng Zhang
Gaili Li
Proteins are fundamental to biological processes, mediating critical functions through precise molecular interactions. The rotational dynamics between ligand atoms and protein binding sites can significantly influence interaction efficacy b...
An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation [0.03%]
一种自适应多阶段和相邻级特征融合网络的脑部肿瘤图像分割方法
Jiwen Zhou,Yulun Wu,Yue Xu et al.
Jiwen Zhou et al.
The segmentation of brain tumor magnetic resonance imaging (MRI) plays a crucial role in assisting diagnosis, treatment planning, and disease progression evaluation. Convolutional neural networks (CNNs) and transformer-based methods have ac...
Jing Zhang,Linan Lu,Runqiang Yu et al.
Jing Zhang et al.
Magnesium is an essential element involved in diverse life activities. The strong polarization and significant charge transfer effects pose challenges to the traditional fixed charge force fields. Here we establish the ABEEM/MM magnesium fo...
SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation [0.03%]
SANNO：一种用于空间转录组注释的图变换器增强最优传输工具
Yuansong Zeng,Yuanze Chen,Ningyuan Shangguan et al.
Yuansong Zeng et al.
The latest progress in spatial transcriptomics has empowered scientists to investigate spatial heterogeneity with single-cell precision. A pivotal yet demanding aspect of spatial transcriptomics data analysis is cell type annotation. Howeve...
Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning [0.03%]
基于扩散模型的多通道EEG表示与预测的早期癫痫发作预警方法
Zekun Jiang,Wei Dai,Qu Wei et al.
Zekun Jiang et al.