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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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  16. 3156

    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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    Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

    Published 2025-05-01
    “…This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. …”
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    Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease by Ying He, Fan Lin, Xin Zheng, Qiaobin Chen, Meng Xiao, Xiaoting Lin, Hongbiao Huang

    Published 2025-06-01
    “…This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD. …”
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