Imperial College London


Faculty of MedicineSchool of Public Health

Research Associate in Chemometrics/Metabolomics



+44 (0)20 7594 9761r.pinto Website




155Norfolk PlaceSt Mary's Campus





The focus of my research is metabolomics data processing and analysis. With that objective I test, develop and apply chemometrics and machine learning methods, mostly programming in Matlab. Currently I am working on the large Combibio and Airwave LC-MS datasets, in order to achieve the best data quality possible, and analyzing it for multiple projects, including cardiovascular disease, ageing and dementia.

I have been developing a method to match metabolomic features in multiple peak-picked untargeted LC-MS datasets. That will allow us to concatenate similarly acquired datasets in order to increase power, or to use a dataset as discovery and another as validation. 



Wu C-T, Wang Y, Wang Y, et al., 2020, Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection, Bioinformatics, Vol:36, ISSN:1367-4803, Pages:2862-2871

Pazoki R, Evangelou E, Mosen-Ansorena D, et al., 2019, GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits, Nature Communications, Vol:10, ISSN:2041-1723

Djekic D, Pinto R, Repsilber D, et al., 2019, Serum untargeted lipidomic profiling reveals dysfunction of phospholipid metabolism in subclinical coronary artery disease, Vascular Health and Risk Management, Vol:15, ISSN:1176-6344, Pages:123-135

Pênčík A, Casanova-Sáez R, Pilařová V, et al., 2018, Ultra-rapid auxin metabolite profiling for high-throughput mutant screening in Arabidopsis, Journal of Experimental Botany, Vol:69, ISSN:0022-0957, Pages:2569-2579

Lomnytska M, Pinto R, Becker S, et al., 2018, Platelet protein biomarker panel for ovarian cancer diagnosis, Biomarker Research, Vol:6, ISSN:2050-7771

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