Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma by Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, Xuemei Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen

“…This study provides novel insights into the molecular subtypes of HCC through the application of robust clustering algorithms based on multi-omics data. The constructed CMLS serves as a valuable tool for early prognostic prediction and for screening potential drug candidates that may enhance the efficacy of immunotherapy, thereby establishing a foundation for personalized treatment strategies in HCC. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…This study explored the effectiveness of common machine learning algorithms in predicting pasture yield of temperate grasslands utilizing Landsat-8 data and ground sample data and provided the valuable support for long-term historical monitoring of pasture resources. …”
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The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. …”
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Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer by Chenxi Wang, Xiangqian Zhao, Dawei Wang, Jinyun Wu, Jizhen Lin, Weiwei Huang, Yangkun Shen, Qi Chen

“…This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. …”
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Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…MethodsUsing data from the Korea National Health and Nutrition Examination Survey, 5 ML algorithms were used for the prediction of high ASCVD risk: logistic regression (LR), support vector machine, random forest, extreme gradient boosting, and light gradient boosting models. …”
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Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder by Shengjie Xiong, Lixin Liao, Meng Chen, Qing Gan

“…The predictive nomogram and drug predictions offer valuable tools for MDD management.…”
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Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…Variable selection was conducted using least absolute shrinkage and selection operator (LASSO) regression. NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Methods We developed a three-step analytical framework combining causal inference, predictive modeling, and burden projection to systematically evaluate modifiable factors associated with migraine. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Diabetes, atrial fibrillation, smoking habits, hypertension, age, and viral load demonstrated a high likelihood of association with stroke in PLWH and should be prioritized when constructing clinical prediction algorithms for HIV-related stroke. Conclusions The most important factors were hypertension and chronic kidney disease, followed by smoking, dyslipidemia, diabetes, HCV, HBV, CD4 count, use of ART, TB, and substance use (cocaine). …”
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Urban tourism management based on artificial neural networks analysis and data mining by Xin Cui

“…Tourists are categorized using K-means clustering algorithms according to their preferences and actions. …”
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THE FEATURES OF QUALITATIVE AND QUANTITATIVE RECONSTRUCTION OF BUCCAL EPITHILIUM CELLULAR COMPOSITION IN A NICOTINE INTOXICATION by N.V. Gasiuk, T.N. Moshel, I.Yu. Popovich

“… To determine the cytological criteria that allow prediction of occurrence and course of inflammation of the oral mucosa and periodontal tissues, we performed cytological study of buccal epithelium of young people who have the habit smoking. …”
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Advancing Evapotranspiration Modeling With Optimized Soil and Canopy Resistance Combinations by Jinfeng Zhao, Shikun Sun, Yali Yin, Yihe Tang, Chong Li, Yongshan Liang, Yubao Wang, Alexander Winkler, Shijie Jiang

“…Despite the availability of several ET products and algorithms, comprehensive evaluations of resistance configurations remain scarce. …”
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Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review by Umile Giuseppe Longo, Martina Marino, Guido Nicodemi, Matteo Giuseppe Pisani, Jacob F. Oeding, Christophe Ley, Rocco Papalia, Kristian Samuelsson

“…Research on healthcare cost predictions, deterministic algorithms, patient satisfaction, protocol studies and upper‐extremity fractures not involving the shoulder were excluded. …”
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Transforming Chiller Plant Efficiency with SC+BAS: Case Study in a Hong Kong Shopping Mall by Fong Ming-Lun Alan, Li Baonan Nelson

“…The application of SC+BAS falls into the realm of advanced Trim/Respond algorithms coupled with sophisticated sequencing algorithms that allow for refined optimization of the chiller operations in response to the dynamic demands of urban infrastructure. …”
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Delineating flood susceptibility zones using novel ensemble models – An application of evidential belief function, relative frequency ratio, and Shannon entropy by Samuel Yaw Danso, Yi Ma, Isaac Yeboah Addo

“…One critical West African city with a history of flooding in Ghana, the Cape Coast Metropolis (CCM), was chosen with flood inventories, comprising 70% training and 30% validation, prepared as the basis for accurate prediction modeling. Furthermore, 13 conditioning parameters were chosen via multicollinearity evaluation. …”
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Artificial Intelligence Precision Recognition and Auxiliary Diagnosis of Dental X-ray Panoramic Images Based on Deep Learning by Liu Riming, Gao Zhenshan

“…Additionally, visualization methods were used to display the model’s prediction results across different lesion scales (small caries, medium caries, large caries). …”
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From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI by Gizem Izmir Tunahan, Goksu Tuysuzoglu, Hector Altamirano

“…Drawing on over 1.3 million ten-minute passive infrared (PIR) sensor observations collected throughout 2023 at the UCL Bartlett Library, we modeled seat-level occupancy using 24 spatial, environmental, and temporal features through advanced machine learning algorithms. Among the models tested, Categorical Boosting (CatBoost) demonstrated the highest predictive performance, achieving a classification accuracy of 72.5%, with interpretability enhanced through SHAP (Shapley Additive exPlanations) analysis. …”
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Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk, Marko Valčić

“…This allows uncertainty to be quantified not as a static estimate, but as a function sensitive to both variable type and prediction horizon. When integrated into routing algorithms, such representations allow for route planning strategies that are not only more reflective of real-world meteorological limitations but also more robust to evolving weather conditions, demonstrated by a 3–7% increase in travel time in exchange for improved safety margins across eight test cases.…”
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Overview of Startups Developing Artificial Intelligence for the Energy Sector by Naiyer Mohammadi Lanbaran, Darius Naujokaitis, Gediminas Kairaitis, Gabrielė Jenciūtė, Neringa Radziukynienė

“…Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. …”
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