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Total Citations
50
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About
Hokeun Kim is a leading researcher in computational proteomics, specializing in mass spectrometry-based peptide identification and data analysis. His major contributions center on developing innovative computational methods that dramatically improve the accuracy and reliability of protein identification from complex biological samples. Kim’s most influential work, the Postexperiment Monoisotopic Mass Filtering and Refinement (PE-MMR) method, published in 2008 with 50 citations, revolutionized the treatment of tandem mass spectrometric data. This technique provides a powerful filtering and refinement approach that enables highly accurate analyses of massive proteomics datasets, addressing a critical bottleneck in the field. By enhancing precursor mass accuracy and reducing false positives in peptide identification, PE-MMR has become a foundational tool for researchers working with LC/MS/MS data. Kim’s work bridges the gap between raw spectral data and meaningful biological interpretation, making him a key figure in advancing high-throughput proteomics. His methodological innovations continue to support discoveries in systems biology, biomarker identification, and personalized medicine, demonstrating lasting impact on how scientists extract reliable information from complex mass spectrometry experiments.
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