日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation

scGACL:一种具有多尺度对比学习的生成对抗网络,用于精确的单细胞RNA测序插补

Jiang, Yanlin; Zhao, Mengyuan; Yan, Jiahui; Tang, Jijun; Guo, Fei

MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction

MultiPert:一种用于单细胞多组学扰动预测的对抗性对齐和双重注意力框架

Zhao, Mengyuan; Tang, Xinyue; Li, Jiawei; Liang, Cheng; Tang, Jijun; Guo, Fei

Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields

推进骨关节炎研究:人工智能在临床、影像和组学领域的作用

Ou, Jingfeng; Zhang, Jin; Alswadeh, Momen; Zhu, Zhenglin; Tang, Jijun; Sang, Hongxun; Lu, Ke

SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture

SVEA:一种利用多通道图像编码和增强型 AlexNet 架构进行结构变异检测的精确模型

Qiu, Taixing; Li, Jiawei; Guo, Yan; Jiang, Limin; Tang, Jijun

ST-deconv: an accurate deconvolution approach for spatial transcriptome data utilizing self-encoding and contrastive learning

ST-deconv:一种利用自编码和对比学习的精确空间转录组数据反卷积方法

Dai, Shurui; Li, Jiawei; Xia, Zhiliang; Ou, Jingfeng; Guo, Yan; Jiang, Limin; Tang, Jijun

HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning

HyperPhS:一种基于对比超图学习的药效团引导的多模态表示框架,用于代谢稳定性预测

Liu, Xiaoyi; Zhang, Na; Kang, Chenglong; Ai, Chengwei; Yang, Hongpeng; Tang, Jijun; Guo, Fei

A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data

一种基于基因调控网络的图学习方法,用于单细胞RNA测序数据中的细胞身份注释

Zhao, Mengyuan; Li, Jiawei; Liu, Xiaoyi; Ma, Ke; Tang, Jijun; Guo, Fei

RecGOBD: accurate recognition of gene ontology related brain development protein functions through multi-feature fusion and attention mechanisms

RecGOBD:通过多特征融合和注意力机制准确识别与基因本体论相关的脑发育蛋白功能

Xia, Zhiliang; Ma, Shiqiang; Li, Jiawei; Guo, Yan; Jiang, Limin; Tang, Jijun

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

利用深度学习和几何形态学进行橡树(Quercus spp.)物种鉴定:优缺点

Qi, Min; Du, Fang K; Guo, Fei; Yin, Kangquan; Tang, Jijun

RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation

RetroCaptioner:超越注意力,通过对比字幕可学习图表示实现端到端逆向合成转换器。

Liu, Xiaoyi; Ai, Chengwei; Yang, Hongpeng; Dong, Ruihan; Tang, Jijun; Zheng, Shuangjia; Guo, Fei