DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis by Hendrik Schippers, Melina Geis, Stefan Bocker, Christian Wietfeld

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GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts by Haochen Chen, Tao Liang, Kai Tan, Anan Wu, Xin Lu

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Identification and validation of LDHA and SLC16A1 for predicting prognosis and diagnosis in lower-grade glioma by Jingyi Huang, Qishun Wang, Zhijun Li, Xuning Huang, Wen Wang, Jiao Liu

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Prediction of the Methane Yield From Extensively Managed, Flower‐Rich Fen Grassland Based on NIRS Data by M. Wendt, S. Nandke, P. Scharschmidt, M. Thielicke, J. Ahlborn, M. Heiermann, F. Eulenstein

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deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria by Yao Xiao, Yan Zhang

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Predicting postoperative motor outcomes in the surgical management of Rolandic focal cortical dysplasia: the role of glucose metabolism by Weiyuan Luo, Bowen Yang, Xiaobin Zhao, Baotian Zhao, Chao Zhang, Xiu Wang, Jiajie Mo, Xiangzhi Lu, Zhong Zheng, Xiaoqiu Shao, Jianguo Zhang, Kai Zhang, Wenhan Hu

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355 Validation of an artificial intelligence Algorithm for predicting diagnosis-related groups in a community health system by Angela Muhanga, King Jason, Xsolis Mueller, Jennifer L. Sanger, Julie M. Bartlett, Brian N. Manz, James W. Spiten, Matthew J. Overgaard, Shauna M. Wang Hanyin

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Post-surgical fall risk prediction: a machine learning approach for spine and lower extremity procedures by Ya-Huei Chen, Xing-Yu Luo, Chia-Hui Chang, Chen-Tsung Kuo, Sou-Jen Shih, Mei-Yu Chang, Mei-Rong Weng, I-Chieh Chen, Ying-Lin Hsu, Ying-Lin Hsu, Jia-Lang Xu

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Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis by Huibo Zhang, Ming Luo, Junwei Feng, Juan Tan, Yan Jiang, Dmitrij Frishman, Yang Liu

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Cluster Analysis of Clinical Remission and Relapse Patterns in Chronic Urticaria: Results from the PREDICT-CU Study by Maria-Magdalena Balp, Irina Pivneva, Andrii Danyliv, Kathleen Chen, Tom Cornwall, Jimmy Royer, James Signorovitch, Dhaval Patil, Ravneet K. Kohli, Thomas Severin, Weily Soong, Alexander M. Marsland

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A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data by Kaiyu Li, Ling Chen, Xinxin Cai, Cai Xu, Yuncheng Lu, Shengfeng Luo, Wenlin Wang, Lizhi Jiang, Guohua Wu

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Fibroblast growth factor receptor risk signature predicts patient prognosis and immunotherapy resistance in colorectal cancer by Xiaofang Li, Zhiling Pan, Zhiling Pan, Tiankuo Luan, Qian Xiao, Liuying Li, Qianxue Wu, Guoqing Yao, Xiang Zhang, Daqiang Song

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Metabolomic-genomic prediction realizes small increases in accuracy of estimated breeding values for daily gain in pigs by Xiangyu Guo, Pernille Sarup, Anders Bay Nord, Mark Henryon, Tage Ostersen, Ole F. Christensen

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A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data by Dailiang Peng, Enhui Cheng, Xuxiang Feng, Jinkang Hu, Zihang Lou, Hongchi Zhang, Bin Zhao, Yulong Lv, Hao Peng, Bing Zhang

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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data by Salha Al-Ahmari, Farrukh Nadeem

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Predicting clinical outcome in posterior circulation large-vessel occlusion patients with endovascular recanalisation: the GNC score by Wei Li, Jing Qiu, Thanh Nguyen, Hui-Sheng Chen, Jia-Qi Wang, Si-Qi Qiu

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Predictive Value of Neutrophil‐to‐Lymphocyte Ratio for Immune Checkpoint Inhibitor‐Related Myocarditis Among Patients Treated for Non‐Small‐Cell Lung Cancer by Jian Xue, Chuanbin Liu, Jun Shao, Li Wang, Yating Han, Jing Wang, Jinda Wang

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NK cell exhaustion in Wilson’s disease revealed by single-cell RNA sequencing predicts the prognosis of cholecystitis by Yong Jin, Jiayu Xing, Chenyu Dai, Lei Jin, Wanying Zhang, Qianqian Tao, Mei Hou, Ziyi Li, Wen Yang, Qiyu Feng, Hongyang Wang, Qingsheng Yu

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Mixture of Experts for Depression and Anxiety Disorder Prediction From Textual and Non-Textual Social Media Data by Wesley Ramos Dos Santos, Ivandre Paraboni, Elton Hiroshi Matsushima, Camila Azevedo Da Silva, Emily Samara De Moura Meira, Joao Victor Rodrigues Ferreira Guimaraes, Julia Da Silva Lins, Laura Enham De Azeredo, Luiz Guilherme Cerqueira Nunes, Vittoria Thiengo Silveira Moreira Rego

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Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis by Doreen Phiri, Frank Makowa, Vivi Leona Amelia, Yohane Vincent Abero Phiri, Lindelwa Portia Dlamini, Min-Huey Chung

“…ConclusionsSocial media textual content can be a useful tool for predicting depression. Demographics, language, social media activity, and temporal features should be considered to maximize the accuracy of depression prediction models. …”
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