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.
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.
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.
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
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WebGestalt: an integrated system for exploring gene sets in various biological contexts
Bing Zhang;Stefan Kirov;Jay Snoddy.
Nucleic Acids Research (2005)
WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013
Jing Wang;Dexter T. Duncan;Zhiao Shi;Bing Zhang.
Nucleic Acids Research (2013)
WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs.
Yuxing Liao;Jing Wang;Eric J Jaehnig;Zhiao Shi.
Nucleic Acids Research (2019)
Proteogenomics connects somatic mutations to signalling in breast cancer
Philipp Mertins;D. R. Mani;Kelly V. Ruggles;Michael A. Gillette;Michael A. Gillette.
Proteogenomic characterization of human colon and rectal cancer
Bing Zhang;Jing Wang;Xiaojing Wang;Jing Zhu.
LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
Suhas V Vasaikar;Peter Straub;Jing Wang;Bing Zhang.
Nucleic Acids Research (2018)
WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit
Jing Wang;Suhas V. Vasaikar;Zhiao Shi;Michael Greer.
Nucleic Acids Research (2017)
Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
Hui Zhang;Tao Liu;Zhen Zhang;Samuel H. Payne.
Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer
J. Joshua Smith;Natasha G. Deane;Fei Wu;Nipun B. Merchant.
GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies.
Bing Zhang;Denise Schmoyer;Stefan Kirov;Jay Snoddy.
BMC Bioinformatics (2004)
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