What is he best known for?
The fields of study he is best known for:
Bing Zhang spends much of his time researching Computational biology, Proteomics, Proteogenomics, Genetics and Proteome.
His Computational biology research includes elements of Statistical hypothesis testing, Saccharomyces cerevisiae, Bioinformatics, Genome and Shotgun proteomics.
His study focuses on the intersection of Proteomics and fields such as Genomics with connections in the field of Visualization, The Internet, Web application and Data integration.
The concepts of his Proteogenomics study are interwoven with issues in Serous fluid, Cancer research, Breast cancer and Proto-oncogene tyrosine-protein kinase Src.
Bing Zhang has included themes like False positive paradox and Cell biology in his Genetics study.
His Proteome research is multidisciplinary, relying on both Gene dosage and Metaproteomics.
His most cited work include:
- WebGestalt: an integrated system for exploring gene sets in various biological contexts (1394 citations)
- WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 (1144 citations)
- Proteogenomic characterization of human colon and rectal cancer (831 citations)
What are the main themes of his work throughout his whole career to date?
Bing Zhang mainly investigates Proteomics, Computational biology, Cancer research, Gene and Genetics.
His Proteomics research incorporates elements of Transcriptome, Proteogenomics and Proteome, Bioinformatics.
His research on Bioinformatics frequently links to adjacent areas such as Visualization.
His Computational biology study integrates concerns from other disciplines, such as Annotation, Epigenomics, Multi omics and Genomics.
His research in Genomics intersects with topics in DNA sequencing, World Wide Web and Systems biology.
He works mostly in the field of Cancer research, limiting it down to topics relating to Colorectal cancer and, in certain cases, Metastasis and Oncology, as a part of the same area of interest.
He most often published in these fields:
- Proteomics (32.02%)
- Computational biology (29.56%)
- Cancer research (18.72%)
What were the highlights of his more recent work (between 2018-2021)?
- Proteomics (32.02%)
- Cancer research (18.72%)
- Computational biology (29.56%)
In recent papers he was focusing on the following fields of study:
Proteomics, Cancer research, Computational biology, Proteogenomics and Cancer are his primary areas of study.
He performs multidisciplinary studies into Proteomics and Phosphoproteomics in his work.
Bing Zhang combines subjects such as Phenotype, Gene, Genetic architecture and Epigenomics with his study of Computational biology.
His research on Genomics and Transcriptome is centered around Proteogenomics.
His Genomics study incorporates themes from Precision medicine and Synthetic lethality.
His work carried out in the field of Cancer brings together such families of science as Proteome, Clinical trial, Serous fluid and Protein digestion.
Between 2018 and 2021, his most popular works were:
- WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. (472 citations)
- Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities. (142 citations)
- Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma (119 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Proteogenomics, Proteomics, Cancer research, Computational biology and Cancer.
The study incorporates disciplines such as Proteome, False positive paradox, Wnt signaling pathway, Identification and Sequence analysis in addition to Proteogenomics.
With his scientific publications, his incorporates both Proteomics and Phosphoproteomics.
His Cancer research study also includes fields such as
- Immune system which intersects with area such as Myeloid,
- Tumor antigen which intersects with area such as Colorectal cancer, Antigen and Suppressor.
His study in Computational biology is interdisciplinary in nature, drawing from both Protein methods, Genome, Reference genome, Genomics and Personalized medicine.
He interconnects Personalized oncology, Selection, Tumor biology, Deep learning and DNA sequencing in the investigation of issues within Genomics.
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