His primary areas of study are Artificial intelligence, Data mining, Machine learning, Genome and Pattern recognition. His study in Random forest and Feature is carried out as part of his Artificial intelligence studies. As a member of one scientific family, Jijun Tang mostly works in the field of Data mining, focusing on Support vector machine and, on occasion, Fingerprint.
His work carried out in the field of Machine learning brings together such families of science as Identification, Information integration and Benchmark. His biological study spans a wide range of topics, including Organism and Computational biology. His Convolutional neural network study in the realm of Pattern recognition interacts with subjects such as Data resolution.
His scientific interests lie mostly in Artificial intelligence, Genome, Computational biology, Algorithm and Pattern recognition. His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning. The Machine learning study combines topics in areas such as Field and Data mining, Identification.
Jijun Tang combines subjects such as Evolutionary biology, Phylogenetics and Inference with his study of Genome. His Computational biology research integrates issues from Ancestral reconstruction, DNA methylation, Spectral clustering, DNA microarray and DNA sequencing. His Pattern recognition study integrates concerns from other disciplines, such as Cross-validation, Filter and Kernel fusion.
His main research concerns Artificial intelligence, Computational biology, Multiple kernel learning, Pattern recognition and Benchmark. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Identification. His study looks at the relationship between Machine learning and topics such as Field, which overlap with Artificial neural network.
His studies deal with areas such as Cancer, Spectral clustering, Proteomics, Whole genome sequencing and CpG site as well as Computational biology. Jijun Tang interconnects Protein structure, Probability distribution and Multivariate statistics in the investigation of issues within Pattern recognition. He focuses mostly in the field of Benchmark, narrowing it down to matters related to ENCODE and, in some cases, Feature selection and Extrapolation.
Artificial intelligence, Multiple kernel learning, Benchmark, Machine learning and Kernel are his primary areas of study. His work in Artificial intelligence covers topics such as Pattern recognition which are related to areas like Multivariate statistics. Jijun Tang has included themes like Gene expression and Epigenetics in his Benchmark study.
His work on Multi-label classification as part of general Machine learning research is frequently linked to Bipartite graph, thereby connecting diverse disciplines of science. His Kernel research also works with subjects such as
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.
Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information
Leyi Wei;Jijun Tang;Jijun Tang;Quan Zou.
Information Sciences (2017)
Steps toward accurate reconstructions of phylogenies from gene-order data
Bernard M. E. Moret;Jijun Tang;Li-San Wang;Tandy Warnow.
Journal of Computer and System Sciences (2002)
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.
Quan Zou;Shixiang Wan;Shixiang Wan;Ying Ju;Jijun Tang;Jijun Tang.
BMC Systems Biology (2016)
Prediction of human protein subcellular localization using deep learning
Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang.
Journal of Parallel and Distributed Computing (2017)
Inversion Medians Outperform Breakpoint Medians in Phylogeny Reconstruction from Gene-Order Data
Bernard M. E. Moret;Adam C. Siepel;Jijun Tang;Tao Liu.
workshop on algorithms in bioinformatics (2002)
Scaling up accurate phylogenetic reconstruction from gene-order data
Jijun Tang;Bernard M. E. Moret.
intelligent systems in molecular biology (2003)
Identification of drug-target interactions via multiple information integration
Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo.
Information Sciences (2017)
Identification of drug-side effect association via multiple information integration with centered kernel alignment
Yijie Ding;Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo.
PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only
Leyi Wei;Pengwei Xing;Jijun Tang;Quan Zou.
IEEE Transactions on Nanobioscience (2017)
Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC
Yinan Shen;Jijun Tang;Jijun Tang;Fei Guo.
Journal of Theoretical Biology (2019)
Profile was last updated on December 6th, 2021.
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