Artificial intelligence, Machine learning, Statistical physics, Data mining and Average path length are his primary areas of study. The study incorporates disciplines such as Sequence and Pattern recognition in addition to Artificial intelligence. His Machine learning research includes themes of Classifier, Subcellular localization and Computational biology.
His Statistical physics research is multidisciplinary, relying on both Statistics, Random walk, Scale-free network and Random graph. The Data mining study combines topics in areas such as Amino acid, Vertex, Theoretical computer science and Cluster analysis. He interconnects Clustering coefficient, Degree and Complex network in the investigation of issues within Average path length.
His primary areas of investigation include Artificial intelligence, Data mining, Machine learning, Statistical physics and Degree distribution. His Artificial intelligence study incorporates themes from Sequence and Pattern recognition. His Data mining study combines topics in areas such as Peer-to-peer, Set, Information retrieval and Cluster analysis.
His research investigates the connection between Statistical physics and topics such as Random walk that intersect with problems in First-hitting-time model. His Degree distribution research includes elements of Discrete mathematics, Average path length, Fractal, Clustering coefficient and Scale-free network. His Average path length study combines topics from a wide range of disciplines, such as Degree and Complex network.
His main research concerns Artificial intelligence, Data mining, Machine learning, Pattern recognition and Computational biology. As a part of the same scientific family, Shuigeng Zhou mostly works in the field of Artificial intelligence, focusing on Sequence and, on occasion, Orientation. The study incorporates disciplines such as Set, Theoretical computer science, Pruning and Protein–protein interaction in addition to Data mining.
His work carried out in the field of Machine learning brings together such families of science as Novelty detection, Semantic consistency and Embedding. His Omics data study in the realm of Computational biology interacts with subjects such as Cellular heterogeneity. His Feature research is multidisciplinary, incorporating perspectives in Scalability, Representation, Index and Exploratory search, Information retrieval.
Shuigeng Zhou focuses on Artificial intelligence, Computational biology, Machine learning, Data mining and Set. His biological study spans a wide range of topics, including Sequence and Pattern recognition. His Computational biology research incorporates themes from Enhancer, Targeted proteomics, Function and Value.
His research in Machine learning intersects with topics in Enhanced Data Rates for GSM Evolution and Constraint. His Data mining study deals with Protein–protein interaction intersecting with Biological system, Protein protein interaction network and Degree. His work deals with themes such as Discrete mathematics, Kernel and Statistics, Regression, Curse of dimensionality, which intersect with Set.
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.
Focusing Attention: Towards Accurate Text Recognition in Natural Images
Zhanzhan Cheng;Fan Bai;Yunlu Xu;Gang Zheng.
international conference on computer vision (2017)
AON: Towards Arbitrarily-Oriented Text Recognition
Zhanzhan Cheng;Yangliu Xu;Fan Bai;Yi Niu.
computer vision and pattern recognition (2018)
GString: A Novel Approach for Efficient Search in Graph Databases
Haoliang Jiang;Haixun Wang;P. S. Yu;Shuigeng Zhou.
international conference on data engineering (2007)
A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation
Qiwen Dong;Shuigeng Zhou;Jihong Guan.
Cassava Genome From a Wild Ancestor to Cultivated Varieties
Wenquan Wang;Binxiao Feng;Jingfa Xiao;Zhiqiang Xia.
Nature Communications (2014)
Distributed Localization Using a Moving Beacon in Wireless Sensor Networks
Bin Xiao;Hekang Chen;Shuigeng Zhou.
IEEE Transactions on Parallel and Distributed Systems (2008)
Improving compound–protein interaction prediction by building up highly credible negative samples
Hui Liu;Jianjiang Sun;Jihong Guan;Jie Zheng.
Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM
Huidong Chen;Luca Albergante;Luca Albergante;Jonathan Y. Hsu;Jonathan Y. Hsu;Caleb A. Lareau;Caleb A. Lareau.
Nature Communications (2019)
Boosting compound-protein interaction prediction by deep learning
Kai Tian;Mingyu Shao;Yang Wang;Jihong Guan.
Shortest path and distance queries on road networks: an experimental evaluation
Lingkun Wu;Xiaokui Xiao;Dingxiong Deng;Gao Cong.
very large data bases (2012)
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