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个人简介
Gerald J. Wyckoff came to the University of Missouri-Kansas City after earning his BS in Biology from Cornell University and his Ph.D. in Genetics from the University of Chicago. His educational background is as a molecular evolutionary geneticist, finding faint signatures of positive selection in a sea of genomic noise. This led him to become involved with large scale genomic, and later proteomic, projects, performing bioinformatics data analysis.
At the University of Missouri-Kansas City, he has developed collaborations with structural biologists and mathematicians, in part to extend his knowledge of how to apply large-scale screening techniques to complex health-related problems. He has helped develop software applications for mass spectrometry relating to analysis of divergent peptides as well as cross-linking analysis. In addition, he has helped create databases for large-scale synthesis of chemical and biological data in species as diverse as fish and humans. This has led to a collaboration called the 1Data project, which has attracted attention from companies that are part of the Animal Health Corridor. All of these problems have as a common theme the development of tools to make sense out of “Big Data” where noise is high and signal is low.
His work has led to the development of software that is being commercialized, and he continues to try and find ways to apply his skills to problems inherent in drug discovery at the interface between animal and Human health.
研究兴趣
论文共 68 篇作者统计合作学者相似作者
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Samson O. Oselusi,Phumuzile Dube, Adeshina I. Odugbemi, Kolajo A. Akinyede, Tosin L. Ilori,Elizabeth Egieyeh,Nicole RS. Sibuyi,Mervin Meyer,Abram M. Madiehe,Gerald J. Wyckoff,Samuel A. Egieyeh
COMPUTERS IN BIOLOGY AND MEDICINE (2024): 107927-107927
JOURNAL OF CLINICAL ONCOLOGYno. 16 (2023)
Journal of Drug Delivery Science and Technology (2023): 104162-104162
Journal of the American Society of Nephrologyno. 11S (2023): 46-46
Journal of Clinical Oncologyno. 16 (2023): 1556-1556
JOURNAL OF CLINICAL ONCOLOGYno. 16 (2023): 8015-8015
Social Science Research Network (2022)
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