Dr. Veselkov's research group focuses on developing computational methods that can make a difference in data-driven global health and disease. He has led the development of a series of field-changing data analytics frameworks to augment emerging molecular ("-omics") profiling technologies into clinical decision support, and population health management. His group expertise combines the use of a diverse range of computational techniques from signal processing, imaging informatics, machine learning, natural language processing and highperformance computing for information extraction from heterogeneous biomedical datasets. His translation interests are precision medicine and digital pathology. Dr Veselkov has received a World Economic Forum (WEF) Young Scientist Award, and currently serves as an active member of the WEF Global Agenda Council on the Future of Computing.
et al., 2014, Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer, Proceedings of the National Academy of Sciences of the United States of America, Vol:111, ISSN:0027-8424, Pages:1216-1221
et al., 2013, Analysis of intact bacteria using rapid evaporative ionisation mass spectrometry, Chemical Communications, Vol:49, ISSN:1359-7345, Pages:6188-6190
et al., 2008, Human metabolic phenotype diversity and its association with diet and blood pressure, Nature, Vol:453, ISSN:0028-0836, Pages:396-U50
et al., 2009, Recursive Segment-Wise Peak Alignment of Biological H-1 NMR Spectra for Improved Metabolic Biomarker Recovery, Analytical Chemistry, Vol:81, ISSN:0003-2700, Pages:56-66
et al., 2010, A Metabolic Entropy Approach for Measurements of Systemic Metabolic Disruptions in Patho-Physiological States, Journal of Proteome Research, Vol:9, ISSN:1535-3893, Pages:3537-3544
et al., 2011, Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery, Analytical Chemistry, Vol:83, ISSN:0003-2700, Pages:5864-5872