Eduardo Chicano-Gálvez is the Coordinator of the Mass Spectrometry and Molecular Imaging Unit (IMSMI) at Maimónides Biomedical Research Institute of Córdoba (IMIBIC; Spain) where he is in charge of optimizations and analysis of mass spectrometry Imaging techniques and the analysis of quantitative proteomics/lipidomics/metabolomics data acquired by LC–MS (DIA, DDA, PRM). Eduardo received his PhD from the University of Córdoba (Spain) and a Bioinformatics Master’s degree from the International University of Andalusia (UNIA) before continuing to work at IMIBIC IMSMI as a Senior Research Technician where he uses proteomics, lipidomics, and metabolomics analysis to investigate a wide range of projects. He has active collaborations with many researchers around the world, focusing mainly in the biomedical area but also in environmental, agricultural and other basic research areas. He is currently working on the finding of new prognosis/diagnosis biomarkers both in fluid and tissue, and developing new approaches combining MSI data and/or LC–MS data using AI, neural networks, classical machine learning methods, and systems biology.
Eduardo Chicano-Gálvez is the Coordinator of the Mass Spectrometry and Molecular Imaging Unit (IMSMI) at Maimónides Biomedical Research Institute of Córdoba (IMIBIC; Spain) where he is in charge of optimizations and analysis of mass spectrometry Imaging techniques and the analysis of quantitative proteomics/lipidomics/metabolomics data acquired by LC–MS (DIA, DDA, PRM). Eduardo received his PhD from the University of Córdoba (Spain) and a Bioinformatics Master’s degree from the International University of Andalusia (UNIA) before continuing to work at IMIBIC IMSMI as a Senior Research Technician where he uses proteomics, lipidomics, and metabolomics analysis to investigate a wide range of projects. He has active collaborations with many researchers around the world, focusing mainly in the biomedical area but also in environmental, agricultural and other basic research areas. He is currently working on the finding of new prognosis/diagnosis biomarkers both in fluid and tissue, and developing new approaches combining MSI data and/or LC–MS data using AI, neural networks, classical machine learning methods, and systems biology.