What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Machine learning
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.
His most cited work include:
- CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. (330 citations)
- A review on machine learning principles for multi-view biological data integration. (186 citations)
- Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks (152 citations)
What are the main themes of his work throughout his whole career to date?
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
- Gene which connect with Systems biology,
- Cross-validation that intertwine with fields like Similarity.
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.
He most often published in these fields:
- Artificial intelligence (33.25%)
- Computational biology (30.42%)
- Data mining (28.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (33.25%)
- Computational biology (30.42%)
- Pattern recognition (15.33%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information (31 citations)
- Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI (19 citations)
- A survey on U-shaped networks in medical image segmentations (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Machine learning
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
- Algorithm which is related to area like Feature extraction,
- Histogram together with Data mining.
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