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. 



Ostman JR, Pinto RC, Ebbels TMD, et al., 2022, Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: a case-control study nested in the Northern Sweden Health and Disease Study, International Journal of Cancer, Vol:151, ISSN:0020-7136, Pages:2115-2127

Climaco Pinto R, Karaman I, Lewis MR, et al., 2022, Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets., Analytical Chemistry, Vol:94, ISSN:0003-2700, Pages:5493-5503

Pazoki R, Elliott J, Evangelou E, et al., 2021, Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes, Nature Communications, Vol:12, ISSN:2041-1723

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

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