What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- Bioinformatics
Weida Tong mainly investigates DNA microarray, Microarray, Computational biology, Bioinformatics and Data science.
His DNA microarray research is multidisciplinary, relying on both Microarray analysis techniques and Reliability.
As part of his studies on Microarray, he often connects relevant areas like Gene expression profiling.
His biological study spans a wide range of topics, including Quantitative structure–activity relationship, Training set, Binding affinities and Estrogen receptor.
His Data science study combines topics in areas such as Data management, Drug repositioning, Knowledge extraction, In silico and Toxicogenomics.
His work on Expression data as part of general Gene expression research is frequently linked to Resource, bridging the gap between disciplines.
His most cited work include:
- The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements (1880 citations)
- The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (648 citations)
- The Estrogen Receptor Relative Binding Affinities of 188 Natural and Xenochemicals: Structural Diversity of Ligands (643 citations)
What are the main themes of his work throughout his whole career to date?
Computational biology, Bioinformatics, DNA microarray, Drug and Genetics are his primary areas of study.
Weida Tong studied Computational biology and Genomics that intersect with Proteomics.
In his study, which falls under the umbrella issue of Bioinformatics, Data mining is strongly linked to Data science.
The study incorporates disciplines such as Microarray, Microarray analysis techniques, Artificial intelligence and Gene expression profiling in addition to DNA microarray.
The Drug study which covers Liver injury that intersects with Biomarker.
His research investigates the connection between Pharmacology and topics such as Estrogen receptor that intersect with issues in Receptor.
He most often published in these fields:
- Computational biology (25.72%)
- Bioinformatics (19.65%)
- DNA microarray (16.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (25.72%)
- Drug (15.03%)
- microRNA (5.49%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Computational biology, Drug, microRNA, Cell biology and Data science.
He has researched Computational biology in several fields, including RNA-Seq, Transcriptome and DNA sequencing, Genome, Reference genome.
The various areas that Weida Tong examines in his RNA-Seq study include Library preparation, Pathway enrichment, Deep sequencing, Microarray and Toxicogenomics.
His studies deal with areas such as In silico, Liver injury, Adverse effect and Intensive care medicine as well as Drug.
Weida Tong interconnects Liver failure, Chronic hepatitis and Bioinformatics in the investigation of issues within Liver injury.
Many of his research projects under Gene expression are closely connected to Age groups with Age groups, tying the diverse disciplines of science together.
Between 2018 and 2021, his most popular works were:
- CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. (30 citations)
- Toxicogenomics: A 2020 Vision. (29 citations)
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods (28 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- Enzyme
His primary areas of study are Drug, Cell biology, microRNA, Regulatory Application and DNA sequencing.
His work deals with themes such as Rhabdomyolysis, Postmarketing surveillance and Distribution, which intersect with Drug.
His microRNA research incorporates elements of Nuclear receptor, Gene expression, Hepatocyte nuclear factors and Long non-coding RNA.
His work carried out in the field of Regulatory Application brings together such families of science as Emerging technologies and Toxicogenomics.
His research combines Computational biology and Read depth.
His Computational biology research focuses on Exact test and how it relates to In silico.
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