Estimation of common breaks in linear panel data models via screening and ranking algorithm by Fuxiao Li, Yanting Xiao, Zhanshou Chen

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Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms by Mingjun Hu, Jie Hao, Jie Wei

“…This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population. …”
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Development of a cataract screening model using an open dataset and deep machine learning algorithms by S.N. Sakhnov, K.D. Axenov, L.E. Axenova, V.V. Vronskaya, A.O. Martsinkevich, V.V. Myasnikova

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MODEL OF ORGANIZATION OF KIDNEY CANCER EARLY DIAGNOSIS by V. G. Moreva, G. N. Alekseeva, P. F. Kiku, L. I. Gurina, K. M. Sabirova, V. N. Rasskazova

“…The model included a population questionnaire to identify risk factors and algorithm of patient routing («roadmap») with suspected kidney cancer for in-depth examination and treatment. …”
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Refining the Feasibility of Machine‐Learning‐Based Diagnostic Model Utilizing Gut Microbiota Analysis for Colorectal Cancer Screening by Shintaro Okumura, Yusuke Konishi, Taku Kitano, Tomonori Matsumoto, Kazutaka Obama, Satoshi Nagayama, Eiji Hara

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Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

“…Objective To establish an early screening model for cervical cancer based on single-cell Raman spectroscopy (SCRS) combined with machine learning algorithms, and to assess the performance of the model. …”
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Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm by Man Wu

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APPLICATION OF THE ENTROPY-PROBABILISTIC MODEL OF THE X-RAY TELEVISION INTROSCOPE OPERATOR IN THE ALGORITHM OF THE COMPUTER SIMULATOR DURING TRAINING OF SECURITY SCREENING POINT EMPLOYEES by A. K. Volkov, V. V. Iudaev, A. V. Dormidontov

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The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting by A.C. Goulart, A.P. Alencar, G. Tunes, L.L.T. Bianchi, M.H. Miname, C.M. Padilha, J.M.S. Pescuma, A.L.C.C. Rodrigues, B.B. Henares, M.S. de Almeida, T.A.O. Machado, D.H. Syllos, Y.P. Wang, M. Rienzo

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Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy by Eduardo Alonso, Andoni Beristain, Jorge Burgos, Ibai Gurrutxaga

“…Various machine learning models, including statistical, linear, and ensemble models, were trained using a pseudo-anonymized dataset of 90,532 screening patients. …”
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Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms by Bruna Carbas, Pedro Sampaio, Sílvia Cruz Barros, Andreia Freitas, Ana Sanches Silva, Carla Brites

“…Similarly, ANN models showed good predictive performance, particularly for FB1 + FB2, with R = 0.99, and the root means square error (RMSE) of 131 μg/kg for calibration; and R = 0.95, RMSE = 656 μg/kg for validation.These findings underscore the efficacy of NIR spectroscopy as a rapid, non-destructive tool for fumonisin screening in maize, with chemometric algorithms enhancing model accuracy, offering a valuable method for ensuring food safety.…”
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Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection—Precision Screening for Lung Cancer by Hsin-Hung Chen, Yun-Ju Wu, Fu-Zong Wu

“…However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. …”
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Deep learning based screening model for hip diseases on plain radiographs. by Jung-Wee Park, Seung Min Ryu, Hong-Seok Kim, Young-Kyun Lee, Jeong Joon Yoo

“…<h4>Conclusion</h4>The deep learning-based model showed high accuracy and reliability in screening hip diseases on plain radiographs, potentially aiding physicians in more accurately diagnosing hip conditions.…”
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Sure Independence Screening for Ultrahigh-Dimensional Additive Model with Multivariate Response by Yongshuai Chen, Baosheng Liang

“…This paper investigated an ultrahigh-dimensional feature screening approach for additive models with multivariate responses. …”
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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

Subjects: “…multi-objective optimization algorithm…”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Based on linear regression (LR), support vector machine (SVM), decision tree (DT) and random forest (RF) algorithms, four intelligent prediction models of cross screening rate were established. …”
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Performance of machine learning-based models to screen obstructive sleep apnea in pregnancy by Jingyu Wang, Wenhan Xiao, Haoyang Hong, Chi Zhang, Min Yu, Liyue Xu, Jun Wei, Jingjing Yang, Yanan Liu, Huijie Yi, Linyan Zhang, Rui Bai, Bing Zhou, Long Zhao, Xueli Zhang, Xiaozhi Wang, Xiaosong Dong, Guoli Liu, Shenda Hong

“…Abstract The purpose of this study is to improve the performance of existing OSA screening tools for pregnant women with machine learning algorithms. …”
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Genomic and algorithm-based predictive risk assessment models for benzene exposure by Minyun Jiang, Minyun Jiang, Na Cai, Na Cai, Juan Hu, Lei Han, Lei Han, Fanwei Xu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Boshen Wang

“…AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. …”
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High-content chemical and RNAi screens for suppressors of neurotoxicity in a Huntington's disease model. by Joost Schulte, Katharine J Sepp, Chaohong Wu, Pengyu Hong, J Troy Littleton

“…By tracking the subcellular distribution of mRFP-tagged pathogenic Huntingtin and assaying neurite branch morphology via live-imaging, we identified suppressors that could reduce Huntingtin aggregation and/or prevent the formation of dystrophic neurites. The custom algorithms we used to quantify neurite morphologies in complex cultures provide a useful tool for future high-content screening approaches focused on neurodegenerative disease models. …”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. …”
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