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  1. Queen Mary University of London
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Publications: MS Beth Hughes

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Wallis R, Hughes BK, Moore M, O'Sullivan EA, McIlvenna LC, Gammon L, Hope A, Bellany F et al. ( 2025 ) . SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells . Aging vol. 17 , ( 11 ) 2688 - 2716 .
Hughes BK, Davis A, Milligan D, Wallis R, Mossa F, Philpott MP, Wainwright LJ, Gunn DA et al. ( 2025 ) . SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden . Genome Medicine vol. 17 , ( 1 )
Hughes BK, Wallis R, Bishop CL ( 2023 ) . Yearning for machine learning: applications for the classification and characterisation of senescence . Cell and Tissue Research vol. 394 , ( 1 ) 1 - 16 .
Hughes BK, Bishop CL ( 2022 ) . Current Understanding of the Role of Senescent Melanocytes in Skin Ageing . Biomedicines vol. 10 , ( 12 )
Wallis R, Milligan D, Hughes B, Mizen H, López-Domínguez JA, Eduputa U, Tyler EJ, Serrano M et al. ( 2022 ) . Senescence-associated morphological profiles (SAMPs): an image-based phenotypic profiling method for evaluating the inter and intra model heterogeneity of senescence . Aging vol. 14 , ( 10 ) 4220 - 4246 .
Tyler EJ, del Arroyo AG, Hughes BK, Wallis R, Garbe JC, Stampfer MR, Koh J, Lowe R et al. ( 2021 ) . Early growth response 2 (EGR2) is a novel regulator of the senescence programme . Aging Cell vol. 20 , ( 3 )
Tyler EJ, del Arroyo AG, Wallis R, Hughes B, Garbe JC, Stampfer MR, Koh J, Lowe R et al. . Early growth response 2 (EGR2) is a novel regulator of the senescence program .
Wallis R, Hughes BK, Moore M, O’Sullivan EA, McIlvenna LC, Gammon L, Hope A, Bellany F et al. . SAMP-Score: A morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells .
Bishop C, Hughes B, Davis A, Milligan D, Wallis R, Philpott M, Wainwright L, Gunn D . SenPred: A single-cell RNA sequencing-based machine learning pipeline to classify senescent cells for the detection of an in vivo senescent cell burden .
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