Bader F Al-Anzi,Nasser B Alkhalifah,Hesham A Almansouri et al.
Bader F Al-Anzi et al.
Mke-resnet: a lightweight and interpretable deep learning framework for efficient RNA m6A site identification [0.03%]
Mke-ResNet:一种轻量级且可解释性强的深度学习框架,用于高效识别RNA m6A位点
Xiao Gao,Jianhua Jia,Cong Hui et al.
Xiao Gao et al.
scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder [0.03%]
具有零膨胀变分自动编码器的单细胞RNA序数据的插补方法-scZiva
Long Tuan Vo,Van Vinh Le,Quoc Toan Ha et al.
Long Tuan Vo et al.
SPICEY: an R package for quantifying tissue specificity from single cell multi-omics data [0.03%]
SPICEY:一个从单细胞多组学数据量化组织特异性的R包
Georgina Fuentes-Páez,Nacho Molina,Mireia Ramos-Rodríguez et al.
Georgina Fuentes-Páez et al.
DAESC + : high-performance, integrated software for single-cell allele-specific expression data [0.03%]
DAESC+ :单细胞基因型特异性表达数据的高性能集成软件
Tengfei Cui,Guanghao Qi
Tengfei Cui
SCW: building the whole-genome 3D structures based on extremely sparse single-cell Hi-C data [0.03%]
基于单细胞Hi-C稀疏数据构建全基因组三维结构
Hao Zhu,Tong Liu,Bishal Shrestha et al.
Hao Zhu et al.
Background: The study of three-dimensional (3D) genome structures at the single-cell level is crucial for understanding cell-to-cell variability. However, it is challenging to reconstruct the 3D structures of the whole ge...
BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines [0.03%]
基于生物知识的多组学数据驱动的癌症药效预测方法BKDRP
Koyel Mandal,Sanghamitra Bandyopadhyay
Koyel Mandal
Background: Cancer heterogeneity results in patients with the same diagnosis responding differently to drugs, making treatments extremely challenging. Advances in computational power enable personalized treatments that su...
Explainable graph learning for multimodal single-cell data integration [0.03%]
用于多模态单细胞数据分析集成的可解释图学习方法
Mehmet Burak Koca,Fatih Erdoğan Sevilgen
Mehmet Burak Koca
Background: Understanding cellular heterogeneity and identifying functionally distinct subpopulations are central goals in single-cell analysis. Integrating paired multi-omic data, such as transcriptomic and proteomic pro...
C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba [0.03%]
基于跨模态跨物种的药物相互作用预测(C2M-Mamba)
Shanwen Zhang,Chuanlei Zhang,Dengwu Wang
Shanwen Zhang