Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We applied six machine learning algorithms to construct a model that can predict distant metastasis of gastric cancer. …”
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Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model by Tingting Hu, Liheng Zhao, Xueling Zhao, Lin He, Xiaoli Zhong, Zhe Yin, Junjie Chen, Yanting Han, Ka Li

“…Abstract Objective The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and integrated maternal physiological data collected during delivery. …”
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Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer by Aiqin Zhao, Sufang Zhou, Xiaoyi Yang, Haiying Lu, Dan Zou, Xuan Zhang, Li Liu

“…To analyze the percentage of invading immune cells, the algorithms CIBERSORT, ESTIMATE, and xCell were used. …”
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Combating Corruption in Sport: Legal Aspect (Lucha contra la corrupción en el deporte: aspecto legal) by Tetiana Kolomoiets, Maxym Tkalych, Petro Melnyk, Bogdan Panchenko, Yuliia Tolmachevska

“…Therefore, legal science has a task to determine the legal algorithms for combating sports corruption both within the country and in the international arena. …”
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Equity in Digital Mental Health Interventions in the United States: Where to Next? by Athena Robinson, Megan Flom, Valerie L Forman-Hoffman, Trina Histon, Monique Levy, Alison Darcy, Toluwalase Ajayi, David C Mohr, Paul Wicks, Carolyn Greene, Robert M Montgomery

“…For products with artificial intelligence/machine learning, maintaining a “human in the loop” as well as prespecified and adaptive analytic frameworks to monitor and remediate potential algorithmic bias can reduce the risk of increasing inequity. …”
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Deep learning-based novel ensemble method with best score transferred-adaptive neuro fuzzy inference system for energy consumption prediction by Birce Dağkurs, İsmail Atacak

“…The proposed method uses deep learning (DL)-based algorithms, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), and gated recurrent units (GRUs) as base predictors. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. …”
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ANALISIS PROSES BERPIKIR KOMPUTASI SISWA PADA MATERI BARISAN ARITMETIKA BERDASARKAN KECERDASAN MAJEMUK by Rosevita Melati, Toto Nusantara, Mochammad Hafiizh

“…Visual-spatial intelligence students excelled at pattern detection throughout the algorithmic thinking level. Students with verbal-linguistic intelligence solved the problem by first understanding the illustration and then abstracting the problem information using mathematical words to create generalizations of general formulas. …”
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Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models by Patrick H. Kelly, Rob Kwark, Harrison M. Marick, Julie Davis, James H. Stark, Harish Madhava, Gerhard Dobler, Jennifer C. Moïsi

“…To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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Estimating volumetric water salinity in a Tibetan endorheic lake using machine learning and remote sensing by Pengju Xu, Kai Liu, Yaling Lin, Xuefei Fu, Chenyu Fan, Chunqiao Song

“…First, we developed a model using machine learning algorithms, with remote sensing data and hydrological and topographical features, to estimate surface water salinity. …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season by E. Salganik, O. Crabeck, O. Crabeck, N. Fuchs, N. Hutter, N. Hutter, P. Anhaus, P. Anhaus, J. C. Landy

“…The increasing sea ice air volume defines the rapid decrease in FYI density, complicates the retrieval of ice thickness from satellite altimeters during the melt season, and underlines the importance of considering air volume and density changes in retrieval algorithms.</p>…”
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Taurine-mediated metabolic immune crosstalk indicates and promotes immunosuppression with anti-PD-1 resistance in bladder cancer by Zhengfang Liang, Fengwei Nong, Fengwei Nong, Zhenjie Li, Runmin Chen, Haoxu Zhao, Yongping Huang, Yongping Huang

“…Immuno-infiltration patterns and immunotherapeutic responsiveness were quantified via algorithms including ESTIMATE and TIDE. Mechanistic validation was achieved through co-culture systems.ResultsScRNA-seq profiling revealed significant perturbations in TM scores across epithelial cells, fibroblasts, and macrophages within the BLCA TME. …”
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Identification of Disulfidptosis-Related Genes and Molecular Subgroups in Rheumatoid Arthritis for Diagnostic Model and Patient Stratification by Liu X, Wang S, Du X, Wang Y, Mo L, Li H, Qu Z, Wang X, Sun J, Li Y, Wang J

“…The machine learning algorithms were used to identify the hub genes. Second, a diagnostic model was constructed for RA based on the hub genes. …”
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Exploring Mechanisms of Lang Qing Ata in Non-Alcoholic Steatohepatitis Based on Metabolomics, Network Pharmacological Analysis, and Experimental Validation by Li S, Zhu H, Zhai Q, Hou Y, Yang Y, Lan H, Jiang M, Xuan J

“…By integrating known databases and target prediction algorithms, which encompassed database-based target prediction, protein-protein interaction networks, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, we unveiled the potential key targets and signaling pathways that these bioactive components might engage with. …”
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Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation by Nasser Alnazari, Omar Ibrahim Alanazi, Muath Owaidh Alosaimi, Ziyad Mohamed Alanazi, Ziyad Mohammed Alhajeri, Khaled Mohammed Alhussaini, Abdulkarim Mekhlif Alanazi, Ahmed Y. Azzam

“…Our methodology included a four-stage machine learning pipeline: data processing, feature preparation, model development using stratified 5-fold cross-validation, and clinical validation. Multiple algorithms were evaluated, with gradient boosting demonstrating superior performance. …”
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SK-TreePCN: Skeleton-Embedded Transformer Model for Point Cloud Completion of Individual Trees from Simulated to Real Data by Haifeng Xu, Yongjian Huai, Xun Zhao, Qingkuo Meng, Xiaoying Nie, Bowen Li, Hao Lu

“…Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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Recent advances in anion exchange membrane technology for water electrolysis: a review of progress and challenges by Abdul Qayoom, Muhammad Shakeel Ahmad, H. Fayaz, Atika Qazi, Jeyraj Selvaraj, Rahadian Zainul, Krismadinata, Nasrudin Abd Rahim, Farruh Atamurotov, Thien Khanh Tran, Basma Souayeh, Natei Ermias Benti

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