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Sökning: WFRF:(Simm Jaak)

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1.
  • Altmae, Signe, et al. (författare)
  • Endometrial transcriptome analysis indicates superiority of natural over artificial cycles in recurrent implantation failure patients undergoing frozen embryo transfer
  • 2016
  • Ingår i: Reproductive BioMedicine Online. - : Elsevier BV. - 1472-6483 .- 1472-6491. ; 32:6, s. 597-613
  • Tidskriftsartikel (refereegranskat)abstract
    • Little consensus has been reached on the best protocol for endometrial preparation for frozen embryo transfer (FET). It is not known how, and to what extent, hormone supplementation in artificial cycles influences endometrial preparation for embryo implantation at a molecular level, especially in patients who have experienced recurrent implantation failure. Transcriptome analysis of 15 endometrial biopsy samples at the time of embryo implantation was used to compare two different endometrial preparation protocols, natural versus artificial cycles, for FET in women who have experienced recurrent implantation failure compared with fertile women. IPA and DAVID were used for functional analyses of differentially expressed genes. The TRANSFAC database was used to identify oestrogen and progesterone response elements upstream of differentially expressed genes. Cluster analysis demonstrated that natural cycles are associated with a better endometrial receptivity transcriptome than artificial cycles. Artificial cycles seemed to have a stronger negative effect on expression of genes and pathways crucial for endometrial receptivity, including ESR2, FSHR, LEP, and several interleukins and matrix metalloproteinases. Significant overrepresentation of oestrogen response elements among the genes with deteriorated expression in artificial cycles (P < 0.001) was found; progesterone response elements predominated in genes with amended expression with artificial cycles (P = 0.0052).
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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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