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Keith A. Baggerly
Researcher at University of Texas MD Anderson Cancer Center
Publications - 193
Citations - 59407
Keith A. Baggerly is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Cancer & Ovarian cancer. The author has an hindex of 65, co-authored 192 publications receiving 50803 citations. Previous affiliations of Keith A. Baggerly include University of Texas Health Science Center at Houston & Harvard University.
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Journal ArticleDOI
The importance of experimental design in proteomic mass spectrometry experiments: Some cautionary tales
TL;DR: Several experimental design issues that can corrupt a dataset are described and can be avoided if attention is paid to potential sources of bias before the experiment is run.
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Serum proteomics profiling--a young technology begins to mature.
TL;DR: Improvements in experimental design and data analysis are outlined that might help serum proteomics to realize its full potential, however, several problems in sensitivity and reproducibility remain unresolved.
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A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples
TL;DR: For the analysis of the data from the First Annual Proteomics Data Mining Conference, the intensities at the following mass to charge (m/z) values were found to be useful discriminators and an overall classification accuracy of 38/41 (92.6%).
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Autocrine Effects of Tumor-Derived Complement
Min Soon Cho,Hernan G. Vasquez,Rajesha Rupaimoole,Sunila Pradeep,Sherry Y. Wu,Behrouz Zand,Hee Dong Han,Cristian Rodriguez-Aguayo,Justin Bottsford-Miller,Jie Huang,Takahito Miyake,Hyun Jin Choi,Heather J. Dalton,Cristina Ivan,Keith A. Baggerly,Gabriel Lopez-Berestein,Anil K. Sood,Vahid Afshar-Kharghan +17 more
TL;DR: A role for tumor-derived complement proteins in promoting tumor growth is identified and they therefore have substantial clinical and therapeutic implications.
Journal ArticleDOI
Gene expression signature of estrogen receptor α status in breast cancer
Martín Carlos Abba,Yuhui Hu,Hongxia Sun,Jeffrey A. Drake,Sally Gaddis,Keith A. Baggerly,Aysegul A. Sahin,C. Marcelo Aldaz +7 more
TL;DR: The integration of the breast cancer comparative transcriptome analysis based on ERα status coupled to the genome-wide identification of high-affinity EREs and GO over-representation analysis, provide useful information for validation and discovery of signaling networks related to estrogen response in this malignancy.