In most of his Programming language studies, his work intersects topics such as Template and Set (abstract data type). His research combines Programming language and Set (abstract data type). Yuedong Yang performs integrative Protein structure and Protein design research in his work. In his study, Yuedong Yang carries out multidisciplinary Protein design and Protein structure research. His Sequence (biology) research extends to Biochemistry, which is thematically connected. Yuedong Yang performs integrative study on Artificial intelligence and Data mining. Yuedong Yang merges Data mining with Artificial intelligence in his study. His study deals with a combination of Protein structure prediction and Protein secondary structure. His work often combines Protein secondary structure and Protein structure prediction studies.
His study on Artificial intelligence is interrelated to topics such as Artificial neural network and Pattern recognition (psychology). Borrowing concepts from Protein secondary structure, Yuedong Yang weaves in ideas under Biochemistry. In his research, he performs multidisciplinary study on Protein secondary structure and Biochemistry. He applies his multidisciplinary studies on Gene and RNA in his research. While working in this field, Yuedong Yang studies both RNA and Gene. In his papers, Yuedong Yang integrates diverse fields, such as Computational biology and Genetics. In his study, Yuedong Yang carries out multidisciplinary Genetics and Computational biology research. In his works, Yuedong Yang conducts interdisciplinary research on Machine learning and Data mining. He merges Data mining with Machine learning in his study.
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Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.
Ying Song;Shuangjia Zheng;Liang Li;Xiang Zhang.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021)
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
Song Ying;Shuangjia Zheng;Liang Li;Xiang Zhang.
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
Yuedong Yang;Eshel Faraggi;Huiying Zhao;Yaoqi Zhou.
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.
Rhys Heffernan;Yuedong Yang;Kuldip K. Paliwal;Yaoqi Zhou.
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
Rhys Heffernan;Kuldip Paliwal;James Lyons;Abdollah Dehzangi.
Scientific Reports (2015)
SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
Eshel Faraggi;Tuo Zhang;Tuo Zhang;Yuedong Yang;Yuedong Yang;Lukasz A. Kurgan;Lukasz A. Kurgan.
Journal of Computational Chemistry (2012)
Specific interactions for ab initio folding of protein terminal regions with secondary structures
Yuedong Yang;Yaoqi Zhou.
Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.
Jack Hanson;Yuedong Yang;Kuldip K. Paliwal;Yaoqi Zhou.
Structural insights into the histone H1-nucleosome complex
Bing-Rui Zhou;Hanqiao Feng;Hidenori Kato;Liang Dai.
Proceedings of the National Academy of Sciences of the United States of America (2013)
Sixty-five years of the long march in protein secondary structure prediction: the final stretch?
Yuedong Yang;Jianzhao Gao;Jihua Wang;Rhys Heffernan.
Briefings in Bioinformatics (2016)
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