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Data Fusion for Precision Medicine11-50 Employees
  • B2B
  • Early Stage
    Startup in initial stages
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Data Fusion for Precision Medicine
Genialis builds in silico models of disease biology to advance discovery & de-risk development of novel therapeutics. We apply a novel machine learning approach to integrate wildly diverse data and predict accurate, actionable insights. Our methods were invented specifically to accommodate the heterogeneity and sparsity that characterizes biomedical data and knowledge bases.

We use the Genialis software platform to analyze your proprietary data, then readily integrated into a broad foundation of public information. Thus we tune our models to answer your questions, whether you wish to, e.g.: decipher mechanism of action from hidden relationships between genes, diseases, drugs, phenotypes and more; augment preclinical models to ensure tolerance and gauge efficacy; stratify patient cohorts by molecular subtype or predicted response profile; or identify new uses for existing drugs to treat orphaned indications.
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Rafael Rosengarten
Avatar for Rafael Rosengarten
PhD, Molecular Biology. 10 years genomics research at Yale, LBNL, Baylor College of Medicine. Co-inventor, j5 software, R&D Top 100
Luka Ausec
Avatar for Luka Ausec
Nejc Škoberne
Avatar for Nejc Škoberne
Co-founded Genialis in 2013. PhD in Computer Science. Background in enterprise IT and information security. Father of 4 boys.
Miha Stajdohar
Avatar for Miha Stajdohar
AI scientist and software architect. Leading Genialis’ data science and engineering teams. Co-developed Orange data mining and Resolwe engine for bioinformatics
Klemen Hrovat
Avatar for Klemen Hrovat
Co-founded and Commercial Operations at Genialis
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