His main research concerns Computational biology, Data mining, Artificial intelligence, Machine learning and Bioinformatics. Fang-Xiang Wu has included themes like DNA microarray, Centrality, Subcellular localization and False positive paradox in his Computational biology study. His Data mining research integrates issues from Protein Interaction Networks, Biological network, Biological data and Cluster analysis.
He combines subjects such as Data integration and Pattern recognition with his study of Artificial intelligence. In the subject of general Machine learning, his work in Deep learning, Convolutional neural network and Artificial neural network is often linked to Matrix decomposition, thereby combining diverse domains of study. His Bioinformatics research includes elements of Complex system, Disease, State and Candidate gene.
Fang-Xiang Wu focuses on Artificial intelligence, Computational biology, Data mining, Machine learning and Cluster analysis. His Artificial intelligence study frequently draws connections to other fields, such as Pattern recognition. His Computational biology research also works with subjects such as
His studies deal with areas such as Biological network and Protein–protein interaction as well as Data mining. His biological study spans a wide range of topics, including Controllability, Distributed computing and Complex network. His work in Cluster analysis is not limited to one particular discipline; it also encompasses Algorithm.
His primary scientific interests are in Artificial intelligence, Computational biology, Pattern recognition, Machine learning and Disease. His study in Computational biology is interdisciplinary in nature, drawing from both Identification, Genome, Gene, microRNA and DNA microarray. His Identification study contributes to a more complete understanding of Data mining.
The various areas that Fang-Xiang Wu examines in his Data mining study include Matching and Biological network. His work in the fields of Machine learning, such as Feature vector, intersects with other areas such as Network topology. Fang-Xiang Wu has researched Disease in several fields, including Cross-validation and Biological data.
His scientific interests lie mostly in Artificial intelligence, Computational biology, Machine learning, Deep learning and Disease. The Artificial intelligence study combines topics in areas such as Graph and Pattern recognition. His studies in Computational biology integrate themes in fields like Contig, Identification, Disease Ontology, Interaction network and Similarity.
His Machine learning research incorporates elements of Gene regulatory network and Drug repositioning. His Disease research is multidisciplinary, relying on both Representation and Inference. His Sequence study also includes fields 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.
CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks.
Yu Tang;Min Li;Jianxin Wang;Yi Pan.
A survey of MRI-based brain tumor segmentation methods
Jin Liu;Min Li;Jianxin Wang;Fangxiang Wu.
Tsinghua Science & Technology (2014)
A review on machine learning principles for multi-view biological data integration.
Yifeng Li;Fang-Xiang Wu;Alioune Ngom.
Briefings in Bioinformatics (2016)
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm
Huimin Luo;Jianxin Wang;Min Li;Junwei Luo.
Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks
Long Cheng;Zeng-Guang Hou;Yingzi Lin;Min Tan.
IEEE Transactions on Neural Networks (2011)
Prediction of lncRNA-disease associations based on inductive matrix completion.
Chengqian Lu;Mengyun Yang;Feng Luo;Fang-Xiang Wu.
Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks
Bolin Chen;Weiwei Fan;Juan Liu;Fang-Xiang Wu.
Briefings in Bioinformatics (2014)
CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases.
Chunyan Fan;Xiujuan Lei;Zengqiang Fang;Qinghua Jiang.
LDAP: a web server for lncRNA-disease association prediction
Wei Lan;Min Li;Kaijie Zhao;Jin Liu.
Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks.
Wei Hao Peng;Wei Hao Peng;Jianxin Wang;Weiping Wang;Qing Liu.
BMC Systems Biology (2012)
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: