DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator [0.03%]
基于扩散变换器的单细胞ATAC测序数据模拟器
Shengze Dong,Songming Tang,Ding Liu et al.
Shengze Dong et al.
Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) allows for deciphering the epigenetic landscape at single-cell resolution. The inaccuracies in annotations and the scarcity of available real datasets hinder the...
A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions [0.03%]
一种可扩展且鲁棒的集成深度学习方法预测药物靶点相互作用
Zhixing Cheng,Qunfang Yan,Dewu Ding et al.
Zhixing Cheng et al.
Accurate identification of drug-target interactions (DTIs) is a crucial step in drug discovery. Computational DTI prediction methods can significantly reduce the time and cost associated with drug development. However, effectively integrati...
A Hypergraph-Based Model for Predicting Potential Drug Combinations in Cancer Therapy [0.03%]
基于超图的癌症治疗中药物组合潜力预测模型
Qi Wang,Zhiheng Zhou,Guiying Yan
Qi Wang
Finding effective drug combinations is a pivotal strategy for enhancing therapeutic efficacy and overcoming drug resistance in complex diseases like cancer. While computational methods have accelerated this discovery, most existing models a...
DEAPLOG: Differential Expression Analysis and Pseudo-Temporal Locating and Ordering of Genes in Single-Cell Transcriptomic Data [0.03%]
基于单细胞转录组数据的差异表达分析及基因伪时间定位和排序（DEAPLOG）
Bao Zhang,Jing Wang,Weiwei Wang et al.
Bao Zhang et al.
Differential expression analysis constitutes a crucial step in the analysis of single-cell transcriptomic data. Numerous statistical methods have been developed to conduct differential expression analysis by addressing the sparsity or heter...
TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity [0.03%]
基于Transformer和Mamba的综合考虑抗原呈递和免疫原性的新生抗原预测方法_trambaHLApan
Yibo Zhu,Xiumin Shi,Lu Wang et al.
Yibo Zhu et al.
Neoantigens, tumor-specific peptides with immunogenic potential, represent pivotal targets for cancer immunotherapy. Existing methods prioritize HLA-peptide binding but often fail to adequately address immunogenicity, limiting their clinica...
Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network [0.03%]
基于异构网络多源特征融合的miRNA-药物相互作用预测方法
Chenyue Lei,Xiujuan Lei,Lian Liu et al.
Chenyue Lei et al.
Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions...
MPMB-DR: Meta-path Integration of Multi-source Biological Information for Drug Repositioning [0.03%]
基于多源生物信息的药物重定位的元路径集成方法MPMB-DR
Xiaoyan Sun,Zhenjie Hou,Wenguang Zhang et al.
Xiaoyan Sun et al.
Conventional approaches to drug discovery often require considerable time and effort. The promising solution is to repurpose existing drugs by identifying new therapeutic roles, thereby enhancing development efficiency. Drug repositioning b...
Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering [0.03%]
基于异构图随机注意神经网络和神经协同过滤的潜在微生物-疾病关联预测方法
Bo Wang,Wenlong Zhao,Xiaoxin Du et al.
Bo Wang et al.
Extensive research has underscored the intricate relationships between microbial communities and human diseases. Delving into these associations enhances our understanding of disease mechanisms and facilitates the development of novel thera...
Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising [0.03%]
基于拓扑结构保存和数据去噪的单细胞RNA序列的可解释多任务分析方法
Shengpeng Yu,Zihan Yang,Tianyu Liu et al.
Shengpeng Yu et al.
The advent of single-cell transcriptome sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the individual cell level, overcoming the limitations of bulk RNA sequencing. However, the explosive growth of scRNA...
Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images [0.03%]
融合细胞类型语义标注和整个切片图像的可解释癌症生存预测
Jinchao Chen,Pei Liu,Chen Chen et al.
Jinchao Chen et al.
Survival prediction involves multiple factors, such as histopathological image data and omics data, making it a typical multimodal task. In this work, we introduce semantic annotations for genes in different cell types based on cell biology...