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Sökning: WFRF:(Boniface Eric)

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1.
  • Brunner, Eric J., et al. (författare)
  • Appetite disinhibition rather than hunger explains genetic effects on adult BMI trajectory
  • 2021
  • Ingår i: International Journal of Obesity. - : Nature Publishing Group. - 0307-0565 .- 1476-5497. ; 45, s. 758-765
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND/OBJECTIVES: The mediating role of eating behaviors in genetic susceptibility to weight gain during mid-adult life is not fully understood. This longitudinal study aims to help us understand contributions of genetic susceptibility and appetite to weight gain.SUBJECTS/METHODS: We followed the body-mass index (BMI) trajectories of 2464 adults from 45 to 65 years of age by measuring weight and height on four occasions at 5-year intervals. Genetic risk of obesity (gene risk score: GRS) was ascertained, comprising 92 BMI-associated single-nucleotide polymorphisms and split at a median (=high and low risk). At the baseline, the Eating Inventory was used to assess appetite-related traits of 'disinhibition', indicative of opportunistic eating or overeating and 'hunger' which is susceptibility to/ability to cope with the sensation of hunger. Roles of the GRS and two appetite-related scores for BMI trajectories were examined using a mixed model adjusted for the cohort effect and sex.RESULTS: Disinhibition was associated with higher BMI (beta = 2.96; 95% CI: 2.66-3.25 kg/m(2)), and accounted for 34% of the genetically-linked BMI difference at age 45. Hunger was also associated with higher BMI (beta = 1.20; 0.82-1.59 kg/m(2)) during mid-life and slightly steeper weight gain, but did not attenuate the effect of disinhibition. CONCLUSIONS: Appetite disinhibition is most likely to be a defining characteristic of genetic susceptibility to obesity. High levels of appetite disinhibition, rather than hunger, may underlie genetic vulnerability to obesogenic environments in two-thirds of the population of European ancestry.
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2.
  • Oldenhof, Martijn, et al. (författare)
  • Industry-Scale Orchestrated Federated Learning for Drug Discovery
  • 2023
  • Ingår i: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. ; 37, s. 15576-15584
  • Konferensbidrag (refereegranskat)abstract
    • To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.
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