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    Perspective: How complex in vitro models are addressing the challenges of predicting drug-induced liver injury by K. Taylor, R. Ram, R. Ram, L. Ewart, C. Goldring, G. Russomanno, G. P. Aithal, T. Kostrzewski, C. Bauch, J. M. Wilkinson, J. M. Wilkinson, S. Modi, J. G. Kenna, J. G. Kenna, J. Bailey, J. Bailey

    Published 2025-02-01
    “…Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market. …”
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  20. 4020

    Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions by Osamu Maruyama, Tsukasa Koga

    Published 2025-06-01
    “…Further advanced methods in generating negative EPIs should further improve prediction accuracy. The source code is available at https://github.com/maruyama-lab-design/CBOEP2 .…”
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