Yaoqi Zhou mostly deals with Protein structure, Artificial intelligence, Crystallography, Protein secondary structure and Algorithm. His work deals with themes such as Atom, Biological system and Statistical physics, which intersect with Protein structure. His Artificial intelligence research incorporates elements of Protein structure prediction, CASP, Machine learning and Pattern recognition.
Yaoqi Zhou has included themes like Protein protein, Ab initio and Protein folding in his Crystallography study. Yaoqi Zhou works mostly in the field of Protein secondary structure, limiting it down to concerns involving Sequence alignment and, occasionally, Threading. The study incorporates disciplines such as Data mining, Cutoff and Protein secondary structure prediction in addition to Algorithm.
His primary scientific interests are in Protein structure, Computational biology, Artificial intelligence, Protein folding and Algorithm. His Protein structure study also includes fields such as
His biological study spans a wide range of topics, including Machine learning, Bioinformatics and Pattern recognition. His work carried out in the field of Protein folding brings together such families of science as Crystallography, Folding and Molecular dynamics. The various areas that he examines in his Algorithm study include Data mining, Root-mean-square deviation, CASP, Accessible surface area and Pairwise comparison.
His primary areas of investigation include Computational biology, Protein structure, RNA, Artificial intelligence and Artificial neural network. His Protein structure study integrates concerns from other disciplines, such as Conserved sequence, Sampling, Reporter gene, RNA splicing and Intron. He combines subjects such as Base pair, Biological system, Sequence and Test set with his study of RNA.
The concepts of his Artificial intelligence study are interwoven with issues in Software and Pattern recognition. His Artificial neural network course of study focuses on Protein structure prediction and Ab initio. His research investigates the connection between Ab initio and topics such as Crystallography that intersect with problems in Dihedral angle.
His primary areas of study are Algorithm, Artificial intelligence, Artificial neural network, Deep learning and Computational biology. His study looks at the relationship between Algorithm and topics such as Recurrent neural network, which overlap with Protein secondary structure, Accessible surface area, Protein structure prediction, Convolutional neural network and Residual. His Artificial intelligence study typically links adjacent topics like Protein structure.
His work on Protein design as part of his general Protein structure study is frequently connected to Fold, thereby bridging the divide between different branches of science. His Artificial neural network study combines topics from a wide range of disciplines, such as Sequence identity and Pattern recognition. As a part of the same scientific study, he usually deals with the Computational biology, concentrating on Test set and frequently concerns with Support vector machine, Discriminative model, Lysine, Cross-validation and Feature.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction
Hongyi Zhou;Yaoqi Zhou.
Protein Science (2009)
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.
Interpreting the folding kinetics of helical proteins.
Yaoqi Zhou;Martin Karplus.
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)
A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes.
Chi Zhang;Song Liu;Qianqian Zhu;Yaoqi Zhou;Yaoqi Zhou.
Journal of Medicinal Chemistry (2005)
Protein binding site prediction using an empirical scoring function
Shide Liang;Chi Zhang;Song Liu;Yaoqi Zhou.
Nucleic Acids Research (2006)
FIRST-ORDER DISORDER-TO-ORDER TRANSITION IN AN ISOLATED HOMOPOLYMER MODEL
Yaoqi Zhou;Carol K. Hall;Martin Karplus.
Physical Review Letters (1996)
Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments
Hongyi Zhou;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.
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)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: