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Yuedong Yang
Researcher at Sun Yat-sen University
Publications - 176
Citations - 7579
Yuedong Yang is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 36, co-authored 154 publications receiving 5232 citations. Previous affiliations of Yuedong Yang include RMIT University & Griffith University.
Papers
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Journal ArticleDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.
Ying Song,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Ruixuan Wang,Huiying Zhao,Yunfei Zha,Jun Shen,Yutian Chong,Yuedong Yang +12 more
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.
Posted ContentDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
Song Ying,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Huiying Zhao,Ruixuan Wang,Yutian Chong,Jun Shen,Yunfei Zha,Yuedong Yang +12 more
TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
Journal ArticleDOI
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
Rhys Heffernan,Kuldip K. Paliwal,James Lyons,Abdollah Dehzangi,Alok Sharma,Jihua Wang,Abdul Sattar,Yuedong Yang,Yaoqi Zhou +8 more
TL;DR: The accuracy of the iterative use of predicted secondary structure and backbone torsion angles and dihedrals based on Cα atoms is higher than those of model structures from current state-of-the-art techniques, suggesting the potentially beneficial use of these predicted properties for model assessment and ranking.
Journal ArticleDOI
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates
TL;DR: This article reports efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area.
Journal ArticleDOI
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.
TL;DR: The application of LSTM‐BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long‐range contacts over a previous window‐based, deep‐learning method SPIDER2.