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  1. 3321

    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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  2. 3322

    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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    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
    “…Subsequently, four survival machine learning models were developed to predict LUSC prognosis. These models were validated in the testing sets and integrated into an online tool to assist in survival prediction. …”
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  5. 3325

    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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