Ville-Petteri Makinen

Associate Professor, Doctor of Science, Master of Science

  • Source: Scopus
  • Calculated based on no. of publications stored in Pure and citations from Scopus
20052021

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Public Profile

Ville-Petteri Mäkinen's main focus is on the molecular determinants of human phenotypes and their significance to chronic morbidity such as dementia, obesity, diabetes and heart disease later in life. His core interest is in Alzheimer’s disease, but he is also maintaining an active role in cardiometabolic and metabolomics research via international collaborations. He holds an EMBL Australia Group Leader position in the South Australian Health and Medical Research Institute (SAHMRI) in Adelaide, and he is affiliated with the Faculaty of Molecular and Biological Sciences at the University of Adelaide. The interest in systems genetics and epidemiology started early in A/Prof Mäkinen's career. In his doctoral thesis from Aalto University in Finland, he demonstrated the detrimental effects of the metabolic syndrome phenotype on the long-term adverse outcomes in type 1 diabetes. At the same time, he developed a method to statistically estimate the robustness of complex human phenotypes, helped develop a high-throughput NMR metabolomics platform, and applied both to a large nation-wide cohort of type 1 diabetes. The software is now available as the Numero R package. Before coming to Australia, A/Prof Mäkinen studied integrative genomics in Prof Xia Yang's group in University of California Los Angeles and published new insight into the causal genetic perturbations of gene regulatory networks that predispose to coronary artery disease. He also developed new methodology that was published recently as part of the Mergeomics web site and R package. In 2014, he was appointed to the prestigious EMBL Australia position at SAHMRI, where he is currently leading a cross-disciplinary research group that focuses on the translation of epidemiological results on dementia and other age-associated diseases into wet lab hypotheses and vice versa.

Keywords

  • Machine learning
  • Metabolomics
  • Genetics
  • systems biology
  • Diabetes
  • Cardiovascular Medicine
  • Dementia
  • Statistical Methodology and Modelling

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