GMC-DMA: GNN-Mamba Co-Contrastive Optimization for Disease-Metabolite Association Prediction [0.03%]
基于GNN-Mamba协同对比优化的疾病代谢物关联预测方法(GMC-DMA)
Jian Zhang,Pengli Lu,Fentang Gao
Jian Zhang
As a product of cellular metabolic activity, the level change of metabolites is closely related to the occurrence and development of diseases, so the prediction of metabolite-disease association is a key issue in biomedical research. Tradit...
scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data [0.03%]
基于对比掩码自编码器的稳健表示学习框架以用于单细胞转录组数据(scRNA-seq)
Xiang Chen,Wenfeng He,Junnan Yu et al.
Xiang Chen et al.
The analysis of single-cell RNA sequencing (scRNA-seq) data is beset by formidable hurdles, including large feature space, widespread sparsity, noise contamination, and inter-batch variability, which collectively compromise the accuracy of ...
Multi-scale Multimodal Representation for Enhanced Survival Prediction in Computational Pathology [0.03%]
计算病理学中用于增强生存预测的多尺度跨模态表示方法
Qingnian Hou,Yuping Sun,Jie Ling et al.
Qingnian Hou et al.
SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics [0.03%]
基于空间转录组的跨物种解码细胞和组织景观的深度学习框架
Zhenghui Wang,Ruoyan Dai,Mengqiu Wang et al.
Zhenghui Wang et al.
Traditional spatial transcriptomics methods typically rely on the direct relationship between spatial location and gene expression data, but they often fail to capture the intricate structures embedded in spatial data. To address this limit...
MLDTA an Ensemble-Driven Multimodal Model with Dynamic Fusion for Drug-Target Affinity Prediction [0.03%]
一种基于集成驱动的多模态药物-靶点亲和力预测模型MLDTA
Xiaohan Mao,Peng Zhang,Xinyu Xu et al.
Xiaohan Mao et al.
Existing drug-target binding affinity (DTA) models still face two major challenges. First, current multimodal approaches often rely on fixed fusion strategies or single model architectures, which limits their ability to adaptively capture t...
EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion [0.03%]
基于多源特征融合的蛋白质错义变异效应预测集成方法EMMVEP
Huiling Zhang,Junwen Huang,Yuetong Li et al.
Huiling Zhang et al.
Missense mutations are common in the coding genome and can alter protein functions. Distinguishing pathogenic from benign variants remains challenging despite computational advances. In the present work, we introduce EMMVEP, an ensemble-bas...
A Framework Integrating Spiking Cortical Circuit Modeling and Simulation-Based Inference to Probe Biomarkers of Cortical Dysfunction in Alzheimer's Disease [0.03%]
一种整合了尖峰皮层电路建模和基于模拟的推理框架 以研究阿尔茨海默病皮层功能障碍的生物标志物
Marta Cárdenas Sánchez,Alejandro Orozco Valero,Juan Miguel García et al.
Marta Cárdenas Sánchez et al.
Understanding circuit-level imbalances in the cortex can yield mechanistic insights into Alzheimer's disease (AD), supporting both diagnosis and therapeutic development. We present a computational framework that integrates the causal interp...
Intelligent Recognition Network for Difficult Airway Based on Multi-View Input and Attention Aggregation [0.03%]
基于多视角输入和注意聚合的智能难气道识别网络
Fan Zhang,Jiaqiang Zhang,Zhaoxiang Zhang et al.
Fan Zhang et al.
scMSDA: A Novel Multi-View Fusion Framework for Single-Cell RNA-seq Data Clustering with Semantic and Distribution Alignment [0.03%]
一种新的单细胞RNA序列数据聚类多视图融合框架，具有语义和分布对齐功能
Congcong Jiang,Wenlan Chen,Yanyan Tan et al.
Congcong Jiang et al.
Single-cell RNA sequencing (scRNA-seq) technology has improved cellular heterogeneity resolution but faces challenges like high dimensionality, sparsity, and technical noise in downstream analysis. Existing methods often treat all negative ...
Multi-Network Co-expression Analysis Enhances Biological Insights from Single-Cell Gene Expression [0.03%]
多网络共表达分析增强对单细胞基因表达的生物学理解
Alicia Gómez-Pascual,Araks Martirosyan,Katja Hebestreit et al.
Alicia Gómez-Pascual et al.