What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Gene
Jian Chen mainly focuses on Genetics, Supply chain, Fuzzy clustering, Cluster analysis and Genome.
When carried out as part of a general Genetics research project, his work on DNA methylation and Gene is frequently linked to work in Heterosis, therefore connecting diverse disciplines of study.
His study in Supply chain is interdisciplinary in nature, drawing from both Supply and demand, Microeconomics, Profit and Commerce.
Jian Chen combines subjects such as Newsvendor model and Operations research with his study of Profit.
His biological study deals with issues like k-means clustering, which deal with fields such as Data mining, CURE data clustering algorithm, Clustering high-dimensional data and Correlation clustering.
Jian Chen has researched Cluster analysis in several fields, including Fuzzy set operations, Fuzzy set and Algorithm.
His most cited work include:
- Approach to Group Decision Making Based on Interval-Valued Intuitionistic Judgment Matrices (325 citations)
- Clustering algorithm for intuitionistic fuzzy sets (289 citations)
- AN OVERVIEW OF DISTANCE AND SIMILARITY MEASURES OF INTUITIONISTIC FUZZY SETS (239 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Microeconomics, Visualization, Supply chain and Human–computer interaction are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Machine learning, Data mining, Computer vision and Pattern recognition.
His work deals with themes such as Cluster analysis and Fuzzy clustering, which intersect with Data mining.
In his research, Economic order quantity is intimately related to Newsvendor model, which falls under the overarching field of Microeconomics.
His Supply chain research includes themes of Commerce, Industrial organization and Incentive.
His Human–computer interaction research incorporates elements of Virtual machine and User interface.
He most often published in these fields:
- Artificial intelligence (14.73%)
- Microeconomics (15.99%)
- Visualization (11.91%)
What were the highlights of his more recent work (between 2018-2021)?
- Pharmacology (5.64%)
- Oxidative stress (5.33%)
- Malondialdehyde (5.02%)
In recent papers he was focusing on the following fields of study:
His main research concerns Pharmacology, Oxidative stress, Malondialdehyde, AMPK and Insulin resistance.
He works mostly in the field of Pharmacology, limiting it down to topics relating to Phosphorylation and, in certain cases, KEAP1 and Downregulation and upregulation, as a part of the same area of interest.
Endocrinology and Internal medicine are the areas that his Oxidative stress study falls under.
His studies in Malondialdehyde integrate themes in fields like Inflammation and Superoxide dismutase.
His Superoxide dismutase research is multidisciplinary, incorporating perspectives in COPD and Nitric oxide synthase.
His Insulin resistance research focuses on subjects like Protein kinase B, which are linked to Insulin.
Between 2018 and 2021, his most popular works were:
- ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries (97 citations)
- High Temporal-Resolution Transcriptome Landscape of Early Maize Seed Development. (44 citations)
- Ellagic acid ameliorates oxidative stress and insulin resistance in high glucose-treated HepG2 cells via miR-223/keap1-Nrf2 pathway (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Gene
His primary areas of study are Oxidative stress, Pharmacology, Insulin resistance, Malondialdehyde and Blood lipids.
He regularly ties together related areas like Fatty liver in his Oxidative stress studies.
His work deals with themes such as Inflammation, COPD, Superoxide dismutase and Nitric oxide synthase, which intersect with Pharmacology.
He has researched Insulin resistance in several fields, including Protein kinase B, Phosphorylation and KEAP1.
Jian Chen interconnects AMPK, Protein kinase A, Downregulation and upregulation, Homeostatic model assessment and Lipid metabolism in the investigation of issues within Protein kinase B.
The concepts of his Malondialdehyde study are interwoven with issues in Steatosis, Liver injury, Nitric oxide, Adipose tissue and IκB kinase.
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