Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space by Peng Xu, Rixu Zang, Zongshui Wang, Zhuo Sun

“…This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. …”
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Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

“…Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. …”
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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. …”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…Subsequently, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were employed to screen CARGs. A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme by Xi Liu, Jinming Song, Zhiming Zhou, Yuting He, Shaochun Wu, Jin Yang, Zhonglu Ren

“…The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. …”
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Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer by Erha Munai, Siwei Zeng, Ze Yuan, Dingyi Yang, Yong Jiang, Qiang Wang, Yongzhong Wu, Yunyun Zhang, Dan Tao

“…Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

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Measuring Optical Scattering in Relation to Coatings on Crystalline X-Ray Scintillator Screens by Matthias Diez, Simon Zabler

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Investigation of the influence of the heterogeneous structure of concrete on its strength by V.M. Volchuk, M.A. Kotov, Ye G. Plakhtii, O.A. Tymoshenko, O.H. Zinkevych

“…This approach allowed screening out low-sensitivity structure features from the multifractal spectrum. …”
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Intelligent screening of narrow anterior chamber angle based on portable slit lamp by Xingru He, Guangzheng Dai, Huixin Che, Chenguang Zhang, Hairu Yan, Yu Dang, Haifeng Dong

“…Despite generalization challenges, portable slit lamps equipped with advanced algorithms show promise for NACA screening.…”
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The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances by A. K. Volkov, L. V. Mironova, S. E. Potapova

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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Additionally, CHARMM software was applied for molecular dynamics simulations to calculate empirical energy functions. AI-driven algorithms such as KarmaDock and DeepDock were utilized for large-scale ligand screening and for improving protein–ligand docking accuracy. …”
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Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Conclusions Targeted screening via the CAPMS 1 and CAPMS 2 models offers a cost-effective solution for stroke screening in African clinics and communities. …”
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Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

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Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. by Huili Dou, Sirui Chen, Fangyuan Xu, Yuanyuan Liu, Hongyang Zhao

“…The multi-scale feature fusion module enhances the model's detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. …”
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Using machine learning algorithms to predict colorectal polyps by Xingjian Xiao, Shiyou Liu, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Xianglong Xu

“…Interpretation: Using non-invasive factors and machine learning algorithms can accurately predict the occurrence of colorectal polyps in individuals with positive initial screening results. …”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Difficulties, possible errors and people’s opinion were also highlighted. Conclussion AI algorithms find their potential application in breast cancer screening, mainly as a supportive tool for radiologists. …”
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Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma by Shiyue He, Wenzhong Peng, Xinyue Hu, Yong Chen

“…LASSO regression analyses were utilized to screen the optimal combination of cuproptosis-related genes for constructing a Cox proportional-hazards model, and the cuproptosis-related risk score (CRRS) was calculated to stratify PTC patients in prognosis. …”
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A risk signature constructed by Tregs-related genes predict the clinical outcomes and immune therapeutic response in kidney cancer by Gang Li, Jingmin Cui, Tao Li, Wenhan Li, Peilin Chen

“…Through the machine learning algorithm—Boruta, the potentially important KTRGs were screened further and submitted to construct a risk model. …”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

“…Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. …”
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