What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Internal medicine
Fei Wang mostly deals with Artificial intelligence, Cell biology, Neuroscience, Machine learning and Myosin.
His Artificial intelligence study combines topics in areas such as Graph, Non-negative matrix factorization, Data mining and Pattern recognition.
Fei Wang works mostly in the field of Cell biology, limiting it down to concerns involving Embryonic stem cell and, occasionally, Cellular differentiation.
Neuroscience is frequently linked to Schizophrenia in his study.
His research investigates the connection between Myosin and topics such as Actin that intersect with issues in Cytoskeleton.
His biological study spans a wide range of topics, including Magnetic resonance imaging, Central nervous system and Brain mapping.
His most cited work include:
- Reversion of the Malignant Phenotype of Human Breast Cells in Three-Dimensional Culture and In Vivo by Integrin Blocking Antibodies (1226 citations)
- Deep learning for healthcare: review, opportunities and challenges. (705 citations)
- Electrical signals control wound healing through phosphatidylinositol-3-OH kinase-γ and PTEN (660 citations)
What are the main themes of his work throughout his whole career to date?
Fei Wang mainly investigates Artificial intelligence, Machine learning, Internal medicine, Neuroscience and Data mining.
The various areas that Fei Wang examines in his Artificial intelligence study include Computer vision and Pattern recognition.
His study connects Endocrinology and Internal medicine.
Neuroscience is closely attributed to Schizophrenia in his work.
He most often published in these fields:
- Artificial intelligence (16.14%)
- Machine learning (6.73%)
- Internal medicine (7.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (16.14%)
- Machine learning (6.73%)
- Chemical engineering (5.88%)
In recent papers he was focusing on the following fields of study:
Fei Wang focuses on Artificial intelligence, Machine learning, Chemical engineering, Internal medicine and Deep learning.
His studies deal with areas such as Graph, MEDLINE and Pattern recognition as well as Artificial intelligence.
His Chemical engineering study combines topics from a wide range of disciplines, such as Supercapacitor and Electrochemistry.
His Electrochemistry study results in a more complete grasp of Electrode.
His studies in Internal medicine integrate themes in fields like Major depressive disorder and Oncology.
His Major depressive disorder study is associated with Neuroscience.
Between 2019 and 2021, his most popular works were:
- High Temperature and High Humidity Reduce the Transmission of COVID-19. (81 citations)
- An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China (66 citations)
- Artificial intelligence in COVID-19 drug repurposing. (58 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Internal medicine
Fei Wang spends much of his time researching Artificial intelligence, Chemical engineering, Data science, Machine learning and Major depressive disorder.
As part of one scientific family, Fei Wang deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the MEDLINE, and often Coronavirus disease 2019.
His Chemical engineering research includes themes of Faraday efficiency and Electrochemistry.
His Machine learning research is multidisciplinary, incorporating elements of Retrospective cohort study and News aggregator.
The concepts of his Major depressive disorder study are interwoven with issues in Bipolar disorder, First episode, Internal medicine, Resting state fMRI and Prefrontal cortex.
His Bipolar disorder research is multidisciplinary, relying on both White matter and Oncology.
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