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Using privacy-preserving federated learning to enable pre-competitive cross-industry knowledge sharing and improve QSAR modelspdf fileEffiris
QSAR 2021 - leveraging data from multiple sources to build QSARs through curation and federated learning in Effiris.pdf fileEffiris
Effiris_Improving in silico secondary pharmacology profiling using federated QSAR modelspdf fileEffiris
2021 Webinar - An overview of Effiris 2.1; An enhanced qualitative model suite for anticipating and mitigating adverse drug reactions – Slides and recordingpdf fileEffiris
Anticipating and mitigating adverse drug reactions through machine learning and privacy-preserving data sharing (infographic)pdf fileEffiris
Building Secondary Pharmacology Models: A Novel Approach to Proprietary Data Transfer.pdf fileEffiris
Building Secondary Pharmacology Models: A Novel Approach to Proprietary Data Transfer - RecordingEffiris
Consortium-led federated QSAR models for secondary pharmacology - preparing the datapdf fileEffiris
From Private Data to Shared Knowledgepdf fileEffiris
Future Developments AOP Networks And Predicting Secondary Pharmacologypdf fileEffirisKaptis
Utilising ChEMBL data for building secondary pharmacology modelspdf fileEffiris
Effiris 1.0 Installation Guidepdf fileEffiris
The Road to Effirispdf fileEffiris
A Case Study in Predicting hERG Activity using Multiple (Q)SARs and Data Sourcespdf fileEffiris
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© 2022 Lhasa Limited | Registered office: Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK Tel: +44 (0)113 394 6020
VAT number 396 8737 77 | Lhasa Limited is registered as a charity (290866)| Company Registration Number 01765239 (England and Wales).

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