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  1. 2281

    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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  2. 2282

    Improving drug-drug interaction prediction via in-context learning and judging with large language models by He Qi, He Qi, Xiaoqiang Li, Chengcheng Zhang, Tianyi Zhao, Tianyi Zhao

    Published 2025-06-01
    “…To further refine predictions, we employ GPT-4 as a discriminator to assess the relevance of predictions generated by multiple LLMs.ResultsDDI-JUDGE achieves the best performance among all models in both zero-shot and few-shot settings, with an AUC of 0.642/0.788 and AUPR of 0.629/0.801, respectively. …”
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    XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

    Published 2025-08-01
    “…The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. …”
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    AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity by Aditya A. Agarwal, James Harrang, David Noble, Kerry L. McGowan, Adrian W. Lange, Emily Engelhart, Miranda C. Lahman, Jeffrey Adamo, Xin Yu, Oliver Serang, Kyle J. Minch, Kimberly Y. Wellman, David A. Younger, Randolph M. Lopez, Ryan O. Emerson

    Published 2025-12-01
    “…Recent advances in deep learning provide opportunities to address this challenge by learning sequence–function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. …”
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  7. 2287

    Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma by Guangliang Duan, Qi Huo, Wei Ni, Fei Ding, Yuefang Ye, Tingting Tang, Huiping Dai

    Published 2025-05-01
    “…Traditional prognostic factors, like tumor, node, and metastasis (TNM) staging, offer limited predictive accuracy. This study aims to identify LUSC subtypes and develop predictive models that have the potential to improve prognosis prediction accuracy and support personalized treatment. …”
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