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Zhiyin Xun, Ph.D. - Publications

Affiliations: 
Chemistry Indiana University, Bloomington, Bloomington, IN, United States 
Area:
Structure analysis of biological systems using mass spectrometry-based techniques
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7 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2009 Xun Z, Kaufman TC, Clemmer DE. Stable isotope labeling and label-free proteomics of Drosophila parkin null mutants. Journal of Proteome Research. 8: 4500-10. PMID 19705877 DOI: 10.1021/Pr9006238  0.682
2008 Xun Z, Kaufman TC, Clemmer DE. Proteome response to the panneural expression of human wild-type alpha-synuclein: a Drosophila model of Parkinson's disease. Journal of Proteome Research. 7: 3911-21. PMID 18683964 DOI: 10.1021/Pr800207H  0.703
2008 Xun Z, Sowell RA, Kaufman TC, Clemmer DE. Quantitative proteomics of a presymptomatic A53T alpha-synuclein Drosophila model of Parkinson disease. Molecular & Cellular Proteomics : McP. 7: 1191-203. PMID 18353766 DOI: 10.1074/Mcp.M700467-Mcp200  0.685
2008 Alves P, Arnold RJ, Clemmer DE, Li Y, Reilly JP, Sheng Q, Tang H, Xun Z, Zeng R, Radivojac P. Fast and accurate identification of semi-tryptic peptides in shotgun proteomics. Bioinformatics (Oxford, England). 24: 102-9. PMID 18033797 DOI: 10.1093/Bioinformatics/Btm545  0.479
2007 Xun Z, Sowell RA, Kaufman TC, Clemmer DE. Lifetime proteomic profiling of an A30P alpha-synuclein Drosophila model of Parkinson's disease. Journal of Proteome Research. 6: 3729-38. PMID 17683129 DOI: 10.1021/Pr0700504  0.68
2007 Xun Z, Sowell RA, Kaufman TC, Clemmer DE. Protein expression in a Drosophila model of Parkinson's disease. Journal of Proteome Research. 6: 348-57. PMID 17203978 DOI: 10.1021/Pr060488O  0.669
2006 Tang H, Arnold RJ, Alves P, Xun Z, Clemmer DE, Novotny MV, Reilly JP, Radivojac P. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics (Oxford, England). 22: e481-8. PMID 16873510 DOI: 10.1093/Bioinformatics/Btl237  0.543
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