What is he best known for?
The fields of study he is best known for:
Feixiong Cheng mainly investigates Drug repositioning, Drug discovery, Computational biology, Drug and Artificial intelligence.
His Drug repositioning research is multidisciplinary, relying on both Proportional hazards model, Network medicine, Inference and Confidence interval.
His work carried out in the field of Drug discovery brings together such families of science as Quantitative structure–activity relationship, Kinase and Apoptosis.
His studies examine the connections between Computational biology and genetics, as well as such issues in Human interactome, with regards to Carcinogenesis.
The concepts of his Drug study are interwoven with issues in Metabolic heterogeneity, Gene knockdown and SNAI1.
His work in Artificial intelligence addresses issues such as Machine learning, which are connected to fields such as Biological network and Repurposing.
His most cited work include:
- admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. (688 citations)
- Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (649 citations)
- Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (649 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computational biology, Drug discovery, Drug repositioning, Cancer research and Cancer.
His research integrates issues of Genomics, Systems pharmacology, Drug and Interactome in his study of Computational biology.
His primary area of study in Interactome is in the field of Human interactome.
His Drug discovery study deals with In silico intersecting with Support vector machine and Data mining.
His studies deal with areas such as Bioinformatics, Deep learning, Inference, Artificial intelligence and Network medicine as well as Drug repositioning.
His studies in Cancer integrate themes in fields like Exome sequencing, Gene and Gene regulatory network.
He most often published in these fields:
- Computational biology (68.36%)
- Drug discovery (45.82%)
- Drug repositioning (44.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (68.36%)
- Network medicine (28.36%)
- Drug repositioning (44.36%)
In recent papers he was focusing on the following fields of study:
Feixiong Cheng mostly deals with Computational biology, Network medicine, Drug repositioning, Interactome and Disease.
His Computational biology research is multidisciplinary, incorporating perspectives in Exome sequencing, Genomics, Small molecule, Human interactome and Drug discovery.
His work focuses on many connections between Drug discovery and other disciplines, such as Systems pharmacology, that overlap with his field of interest in Systems biology and Biological network.
His study on Drug repositioning is covered under Drug.
His study in the field of Antiviral drug is also linked to topics like Coronavirus.
His work deals with themes such as Chemokine and Pathogenesis, which intersect with Interactome.
Between 2019 and 2021, his most popular works were:
- Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (649 citations)
- Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (649 citations)
- Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (649 citations)
In his most recent research, the most cited papers focused on:
His main research concerns Drug repositioning, Network medicine, Artificial intelligence, Interactome and Drug discovery.
His Drug repositioning research is included under the broader classification of Drug.
Feixiong Cheng interconnects Inflammatory bowel disease, Observational study, Propensity score matching and Oncology in the investigation of issues within Network medicine.
His Artificial intelligence research incorporates elements of Machine learning, Biological network, MEDLINE and Big data.
His study looks at the intersection of Interactome and topics like Systems pharmacology with Repurposing, Antiviral drug and Human interactome.
He has included themes like Drug target and Feature vector in his Drug discovery study.
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