MRI-based habitat analysis of vascular and nerve invasion in the tumor microenvironment: an advanced approach for prostate cancer diagnosis by Bo Guan, Cong Huang, Yalei Wang, Jialong Zhang, Xiaowei Li, Zongyao Hao, Zongyao Hao

“…Finally, we assessed the performance of these features using the DeLong test (through the area under the receiver operating characteristic curve, AUC), evaluated the calibration curve with the Hosmer-Lemeshow test, and performed decision curve analysis.ResultsIn the training set, the optimal algorithm based on the intratumoral heterogeneity score had an AUC value of 0.882 (CI: 0.843-0.921); in the test set, the AUC value was 0.860 (CI: 0.792-0.928). …”
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A method for the identification of lactate metabolism-related prognostic biomarkers and its validations in non-small cell lung cancer by Weiyang Yang, Miao Gu, Yabin Zhang, Yunfan Zhang, Tao Liu, Di Wu, Juntao Deng, Min Liu, Youwei Zhang

“…We proposed a Cox elastic-net regression combined with genetic algorithm (GA-EnCox) to predict prognosis and optimize the selection of key biomarkers. …”
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Identification and functional analysis of energy metabolism and pyroptosis-related genes in diabetic nephropathy by Shan He, Jian Ye, Yu Wang, Lu yang Xie, Si Yi Liu, Qin kai Chen

“…Immune cell infiltration in DN was assessed using the ssGSEA algorithm, and the expression levels of EMAPRDEGs in DN tissues were validated by quantitative real-time PCR (qRT-PCR). …”
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Significance of Immune-Related Genes in the Diagnosis and Classification of Intervertebral Disc Degeneration by Bo Wu, Xinzhou Huang, Mu Zhang, Wei Chu

“…The nomogram model revealed that decision-making based on an established model might be beneficial for IDD patients, and the predictive power of the nomogram model was significant. …”
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Co‐Expression of MHC‐II and ANXA1: Mediators of PD‐1/PD‐L1 Therapy Resistance in Breast Cancer by Hao Wang, Ji‐Feng Sun, Chen Wang, Zhan‐Sheng Jiang, Zhong‐Sheng Tong

“…ANXA1 could serve as both a predictive marker for treatment resistance and a therapeutic target to enhance the efficacy of immunotherapy.…”
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Global change drives potential niche contraction and range shift of globally threatened African vulture by Gemechis B. Mosisa, Nega Tassie, Fatuma Yesuf

“…This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. …”
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Experimental and computational evaluation of knee implant wear and creep under in vivo and ISO boundary conditions by Michael J. Dreyer, Seyyed Hamed Hosseini Nasab, Philippe Favre, Fabian Amstad, Rowena Crockett, William R. Taylor, Bernhard Weisse

“…Future work should focus on expanding the range of daily activities simulated and addressing model sensitivity to contact mechanics to further enhance predictive accuracy.…”
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MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures by Wenhua Liang, Hong Yu, Lisha Duan, Xiaona Li, Ming Wang, Bing Wang, Jianling Cui

“…Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. …”
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Exploring machine learning classification for community based health insurance enrollment in Ethiopia by Seyifemickael Amare Yilema, Seyifemickael Amare Yilema, Yegnanew A. Shiferaw, Yikeber Abebaw Moyehodie, Setegn Muche Fenta, Denekew Bitew Belay, Denekew Bitew Belay, Haile Mekonnen Fenta, Haile Mekonnen Fenta, Teshager Zerihun Nigussie, Ding-Geng Chen, Ding-Geng Chen

“…Therefore, this study aimed to identify the ML algorithm with the best predictive accuracy for CBHI enrollment and to determine the most influential predictors among the dataset.MethodsThe 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. …”
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A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…In the coarse screening procedure, the proposed sample selection strategy divides the pneumoconiosis dataset into ‘confident’ and ‘less-confident’ subsets based on the logical relationship between the prediction correctness and confidence of multiple expert networks. …”
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Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis by Caleb J. Thomson, W. Caden Hamrick, Jakob W. Travis, Michael D. Adkins, Patrick P. Maitre, Steven R. Edgely, Jacob A. George

“…In the adjacent field of myoelectric prostheses, regression algorithms have enabled simultaneous and proportional position control over multiple degrees of freedom (DOFs), which in turn has improved user dexterity. …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

“…Understanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making. …”
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Design and refinement of a clinical trial staffing model within the evolving landscape of oncology clinical trials by Ellen Siglinsky, Hannah Phan, Silviya Meletath, Amber Neal, David E. Gerber, Asal Rahimi, Erin L. Williams

“…Methods: To address individual protocol acuity, the model's algorithms include metrics to account for visit frequency, and the quantity, and types of research-related procedures. …”
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Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach by Philemon Tsele, Abel Ramoelo, Lucy Moleleki, Sunette Laurie, Whelma Mphela, Natasha Tshuma

“…However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. …”
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Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network by Y. Lei, Y. L. Tang, J. X. Wang, Y. Q. Jiang, Y. Luo

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Novel multi-omics analysis revealing metabolic heterogeneity of breast cancer cell and subsequent development of associated prognostic signature by Peng Zhang, Cuicui Li, Fen Li, Jiezhong Wu, Kunpeng Hu, He Huang

“…Drug sensitivity prediction was performed via the OncoPredict tool. Functional assays investigated the oncogenic role of PDCD1 in breast cancer cell lines. …”
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A multi-model approach for estimation of ash yield in coal using Fourier transform infrared spectroscopy by Sameeksha Mishra, Anup K. Prasad, Arya Vinod, Anubhav Shukla, Shailayee Mukherjee, Bitan Purkait, Atul K. Varma, Bhabesh C. Sarkar

“…Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. …”
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Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…In terms of disease prediction, one study had an area under the curve of 0.790 for cataract surgery risk prediction. …”
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