What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Statistics
His main research concerns Artificial intelligence, Data mining, Computational biology, Machine learning and Centrality.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Mri image and Pattern recognition.
Jianxin Wang interconnects Interaction network, Similarity, Biological network and Clustering coefficient in the investigation of issues within Data mining.
His research in Interaction network focuses on subjects like Cluster analysis, which are connected to Theoretical computer science.
His Computational biology study integrates concerns from other disciplines, such as Genetics, Bioinformatics, Genomics, DNA microarray and Disease.
His research in Centrality intersects with topics in Value and Network topology.
His most cited work include:
- CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. (330 citations)
- Identification of Essential Proteins Based on Edge Clustering Coefficient (185 citations)
- Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. (180 citations)
What are the main themes of his work throughout his whole career to date?
Jianxin Wang mainly focuses on Artificial intelligence, Computer network, Computational biology, Algorithm and Combinatorics.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
His biological study spans a wide range of topics, including Throughput and Distributed computing.
His Computational biology course of study focuses on Data mining and Centrality.
His Algorithm study frequently draws connections to other fields, such as Set.
Many of his studies involve connections with topics such as Discrete mathematics and Combinatorics.
He most often published in these fields:
- Artificial intelligence (17.04%)
- Computer network (16.79%)
- Computational biology (15.19%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (17.04%)
- Computational biology (15.19%)
- Pattern recognition (7.28%)
In recent papers he was focusing on the following fields of study:
Jianxin Wang mostly deals with Artificial intelligence, Computational biology, Pattern recognition, Machine learning and Algorithm.
His Computational biology research is multidisciplinary, relying on both Genome, Disease and Identification.
He has included themes like Cross-validation and Biological data in his Disease study.
He performs multidisciplinary studies into Machine learning and Drug repositioning in his work.
His work on Set expands to the thematically related Algorithm.
Similarity connects with themes related to Cluster analysis in his study.
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
- Computer network
- Statistics
Jianxin Wang focuses on Artificial intelligence, Computational biology, Disease, Machine learning and Pattern recognition.
His research investigates the connection between Artificial intelligence and topics such as Identification that intersect with problems in Centrality.
His Computational biology research incorporates elements of DNA methylation, Genome, Disease Ontology, Interaction network and Similarity.
His Interaction network study combines topics in areas such as Matching, Proteomics and Data modeling.
Jianxin Wang combines subjects such as Mutual information and Regulator gene with his study of Machine learning.
His Pattern recognition research includes themes of Functional magnetic resonance imaging and Key.
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.
