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  1. 1
    Essentiality, protein–protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning

    Essentiality, protein–protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning von Safadi, Amro, Lovell, Simon C., Doig, Andrew J.

    Veröffentlicht in Scientific reports

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  2. 2
    Using an integrative machine learning approach utilising homology modelling to clinically interpret genetic variants: CACNA1F as an exemplar

    Using an integrative machine learning approach utilising homology modelling to clinically interpret genetic variants: CACNA1F as an exemplar von Sallah, Shalaw R, Sergouniotis, Panagiotis I, Barton, Stephanie, Ramsden, Simon, Taylor, Rachel L, Safadi, Amro, Kabir, Mitra, Ellingford, Jamie M, Lench, Nick, Lovell, Simon C, Black, Graeme C M


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  3. 3
    Distinctive Characteristics of Cancer-associated Genes and Point Mutations Driving Carcinogenesis Through Computational Modelling

    Distinctive Characteristics of Cancer-associated Genes and Point Mutations Driving Carcinogenesis Through Computational Modelling von Safadi, Amro

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