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Jiahua Rao

Showing results (1-10 of 20) with videos related to

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Patterns (New York, N.Y.)|December 26, 2022
Integrating supercomputing and artificial intelligence for life scienceJiahua Rao, Shuangjia Zheng, Yuedong Yang
Patterns (New York, N.Y.)|December 26, 2022
Quantitative evaluation of explainable graph neural networks for molecular property predictionJiahua Rao, Shuangjia Zheng, Yutong Lu, et al.
Iscience|September 6, 2023
Gene based message passing for drug repurposingYuxing Wang, Zhiyang Li, Jiahua Rao, et al.
Iscience|May 17, 2021
Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networksJiahua Rao, Xiang Zhou, Yutong Lu, et al.
Journal of Chemical Information and Modeling|March 14, 2024
Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge GraphJiancong Xie, Yi Wang, Jiahua Rao, et al.
Journal of Chemical Information and Modeling|December 12, 2019
Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural NetworksShuangjia Zheng, Jiahua Rao, Zhongyue Zhang, et al.
Computers in Biology and Medicine|February 4, 2024
Predicting disease-gene associations through self-supervised mutual infomax graph convolution networkJiancong Xie, Jiahua Rao, Junjie Xie, et al.
Plos Computational Biology|May 29, 2026
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposingJiafang Li, Yifei Li, Siying Lin, et al.
Computers in Biology and Medicine|May 14, 2021
Integrating multi-omics data through deep learning for accurate cancer prognosis predictionHua Chai, Xiang Zhou, Zhongyue Zhang, et al.
Cell Systems|March 20, 2026
Context-informed subgraph foundation models enable interpretable protein-function predictionZhuomin Zhou, Jiahua Rao, Zhongyue Zhang, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
Patterns (New York, N.Y.)|December 26, 2022
Integrating supercomputing and artificial intelligence for life scienceJiahua Rao, Shuangjia Zheng, Yuedong Yang
Patterns (New York, N.Y.)|December 26, 2022
Quantitative evaluation of explainable graph neural networks for molecular property predictionJiahua Rao, Shuangjia Zheng, Yutong Lu, et al.
Iscience|September 6, 2023
Gene based message passing for drug repurposingYuxing Wang, Zhiyang Li, Jiahua Rao, et al.
Iscience|May 17, 2021
Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networksJiahua Rao, Xiang Zhou, Yutong Lu, et al.
Journal of Chemical Information and Modeling|March 14, 2024
Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge GraphJiancong Xie, Yi Wang, Jiahua Rao, et al.
Journal of Chemical Information and Modeling|December 12, 2019
Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural NetworksShuangjia Zheng, Jiahua Rao, Zhongyue Zhang, et al.
Computers in Biology and Medicine|February 4, 2024
Predicting disease-gene associations through self-supervised mutual infomax graph convolution networkJiancong Xie, Jiahua Rao, Junjie Xie, et al.
Plos Computational Biology|May 29, 2026
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposingJiafang Li, Yifei Li, Siying Lin, et al.
Computers in Biology and Medicine|May 14, 2021
Integrating multi-omics data through deep learning for accurate cancer prognosis predictionHua Chai, Xiang Zhou, Zhongyue Zhang, et al.
Cell Systems|March 20, 2026
Context-informed subgraph foundation models enable interpretable protein-function predictionZhuomin Zhou, Jiahua Rao, Zhongyue Zhang, et al.
Pageof 2