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Y
Young‐Rae Cho
H-index:22
Citation Count:1,429
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ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification
ORBIT：基于双原型对比学习在双曲空间中进行致癌基因表示学习以识别癌症驱动基因
TriDTI: tri-modal representation learning with cross-modal alignment for drug–target interaction prediction
TriDTI: 用于药物-靶点相互作用预测的三模态表示学习与跨模态对齐
GRAFT: a graph-aware fusion transformer for cancer driver gene prediction
GRAFT：一种用于癌症驱动基因预测的图感知融合变换器
MEMOL: Mixture of experts for multimodal learning through multi-head attention to predict drug toxicity
MEMOL：通过多头注意力机制实现的多模态学习专家混合模型，用于预测药物毒性
Conductive Metal-Organic Frameworks Anchoring on V3O7H2O Nanobelts Toward High-Capacity and Long-Life Zinc-Ion Batteries
导电金属有机框架锚定在V₃O₇·H₂O纳米带上的高容量长寿命锌离子电池
Draw plus : network-based computational drug repositioning with attention walking and noise filtering
Draw plus：基于网络的计算药物重定位方法，结合注意力行走和噪声过滤
Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning
通过张量分解预测药物-基因-疾病关联，用于基于网络的计算药物重新定位
BIOMEDICINES
IF3.9
Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions
用于预测药物-靶标相互作用的基于网络的方法和机器学习算法的比较分析
METHODS
IF4.3
