Pharmacokinomic Profiling Using Patient-Derived Cell Lines Predicts Sensitivity to Imatinib in Dermatofibrosarcoma Protuberans by Rei Noguchi, Takuya Ono, Kazuki Sasaki, Mari Masuda, Akira Kawai, Yuki Yoshimatsu, Tadashi Kondo

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Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies by Ejay Nsugbe, Oluwarotimi Williams Samuel, Jose Javier Reyes-Lagos, Dawn Adams, Olusayo Obajemu

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Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery: A Machine Learning Approach by Beatriz Lau, Daniel Ramos, Vera Afreixo, Luís M. Silva, Ana Helena Tavares, Miguel Martins Felgueiras, Diana Castro Paupério, João Firmino-Machado

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Biomarkers in predicting mortality from cardiovascular events in patients with heart failure and an implanted cardioverter-defibrillator by N. N. Ilov, O. V. Petrova, D. K. Tverdokhlebova, I. R. Karimov, A. A. Nechepurenko, S. A. Boytsov

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Accurate prediction of the lymph node status in ampullary duodenal carcinoma: potential guidance for clinical management by Ran Ni, Tianpeng Zhang, Yixuan Mou, Zhiming Hu, Zongting Gu

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Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma by Jianwei Liu, Xiwen Liao, Xianze Zhu, Peizhen Lv, Rong Li

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Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions by R. Laker, T. S. Horbury, H. O’Brien, E. J. Fauchon‐Jones, V. Angelini, N. Fargette, T. Amerstorfer, M. Bauer, C. Möstl, E. E. Davies, J. A. Davies, R. Harrison, D. Barnes, M. Dumbović

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General practitioners’ predictions of their own patients’ health literacy: a cross-sectional study in Belgium by Hannelore Storms, Frank Vandenabeele, Neree Claes

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Predicting amyloid status in mild cognitive impairment: the role of semantic intrusions combined with plasma biomarkers by Yao Lu, Liang Cui, Lin Huang, Fang Xie, Qi-Hao Guo

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Automated yield prediction in vineyard using RGB images acquired by a UAV prototype platform by Giorgia Orlandi, Alessandro Matese, Alessandro Ulrici, Rosalba Calvini, Andrea Berton, Salvatore Filippo Di Gennaro

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Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions by Baber Ali, Tristan Mary‐Huard, Alain Charcosset, Laurence Moreau, Renaud Rincent

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Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu, Baiwei Feng

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A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

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Improving Tornado Intensity Prediction by Assimilating Radar-Retrieved Vortex Winds After Vortex Relocation by Qin Xu, Kang Nai, Li Wei, Nathan Snook, Yunheng Wang, Ming Xue

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Tendency to overeat predicts an elevated body mass index trajectory across school-age years by Catharina Sarkkola, Sohvi Lommi, Kris Elomaa, Eero Kajantie, Satu Männistö, Heli Viljakainen

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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

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Relativistic Electron Flux Prediction at Geosynchronous Orbit Based on the Neural Network and the Quantile Regression Method by Hui Zhang, Suiyan Fu, Lun Xie, Duo Zhao, Chao Yue, Zuyin Pu, Ying Xiong, Tong Wu, Shaojie Zhao, Yixin Sun, Bo Cui, Zhekai Luo

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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

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Study on the relationship between surface water and groundwater transformation in the middle and lower reaches of Songhua river basin by Xiao Yang, Changlei Dai, Jianyu Jing, Gengwei Liu, Qing Ru, Jiajun Li, Peixian Liu

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Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions by Yu Zhou, Jing Li, Xiaomei Hou, Zhen Li, Yanan Xu, Yan Wang, Mingze Sun, Fumin Zheng, Enhui Guo, Jun Zhou

“…Results: Analysis of existing studies demonstrates that AI models outperform conventional statistical methods in predicting delivery modes, highlighting their potential as valuable tools in obstetric diagnosis and clinical decision-making. …”
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