What is he best known for?
The fields of study he is best known for:
- Gene
- Cancer
- Internal medicine
David T.W. Wong mostly deals with Saliva, Immunology, Salivary diagnostics, Cancer and Bioinformatics.
The study incorporates disciplines such as Proteome, Proteomics, Molecular biology, Disease and Biomarker in addition to Saliva.
His research in Immunology focuses on subjects like Mouth neoplasm, which are connected to Multiplex.
His research in Salivary diagnostics tackles topics such as Intensive care medicine which are related to areas like Salivary biomarkers, Intervention, Blood drawing and Molecular biomarkers.
His Cancer study combines topics in areas such as Transcriptome, Oncology and Genotype.
The Internal medicine study combines topics in areas such as Endocrinology and Pharmacology.
His most cited work include:
- Salivary microRNA: Discovery, Characterization, and Clinical Utility for Oral Cancer Detection (591 citations)
- Multiple regions within 8q24 independently affect risk for prostate cancer. (591 citations)
- Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles (565 citations)
What are the main themes of his work throughout his whole career to date?
David T.W. Wong spends much of his time researching Saliva, Internal medicine, Pathology, Cancer and Molecular biology.
His research on Saliva also deals with topics like
- Disease most often made with reference to Immunology,
- Bioinformatics that intertwine with fields like Computational biology.
His work carried out in the field of Internal medicine brings together such families of science as Endocrinology and Oncology.
His Cancer study deals with Cancer research intersecting with Epidermal growth factor receptor.
His Molecular biology research includes elements of RNA, Gene expression, Messenger RNA and Gene.
His biological study spans a wide range of topics, including Proteomics and Intensive care medicine.
He most often published in these fields:
- Saliva (31.57%)
- Internal medicine (17.88%)
- Pathology (16.78%)
What were the highlights of his more recent work (between 2015-2021)?
- Saliva (31.57%)
- Liquid biopsy (5.30%)
- Internal medicine (17.88%)
In recent papers he was focusing on the following fields of study:
David T.W. Wong focuses on Saliva, Liquid biopsy, Internal medicine, Cancer research and Cancer.
His Saliva research includes themes of Molecular biology, Transcriptome and Immunology.
His study looks at the intersection of Internal medicine and topics like Oncology with Extracellular RNA.
His research integrates issues of Microvesicles, microRNA, Circulating tumor DNA and Egfr mutation in his study of Cancer research.
In his research, Pathology, Case-control study and Disease is intimately related to Biomarker, which falls under the overarching field of Cancer.
His Salivary diagnostics research integrates issues from Precision medicine, Medical physics and Bioinformatics.
Between 2015 and 2021, his most popular works were:
- Saliva diagnostics - Current views and directions (127 citations)
- exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids (89 citations)
- Differential Proteomic Analysis of Human Saliva using Tandem Mass Tags Quantification for Gastric Cancer Detection (65 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Cancer
- Internal medicine
David T.W. Wong mainly focuses on Saliva, Salivary diagnostics, Liquid biopsy, Cancer and Internal medicine.
His Saliva research is multidisciplinary, relying on both Microvesicles, Proteome, Immunology and Intensive care medicine.
His Immunology research is multidisciplinary, incorporating elements of Salivary gland and Cell signaling.
The concepts of his Salivary diagnostics study are interwoven with issues in Precision medicine, Medical physics and Bioinformatics.
David T.W. Wong studies Cancer, namely Carcinogenesis.
His research investigates the link between Internal medicine and topics such as Oncology that cross with problems in Biomarker.
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