Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning by Runze Liu, Ziwei Wang, Yanbo Zhang, Xulong Yao, Shaohong Yan, Zhiyuan Chen, Shuai Wang, Hua Li, Qi Wang

“…Comparative validation indicates that this comprehensive model outperforms traditional methods and basic deep learning algorithms in both prediction efficiency and accuracy. …”
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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

“…We developed a machine learning framework integrating 12 algorithms to establish a consistent VM-related signature (VRG_score). …”
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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

“…A metabolic risk signature was constructed using machine learning algorithms. Immune cell infiltration and immune checkpoint profiles were assessed to explore tumor microenvironment differences. …”
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Bioinformatics analysis of comorbid mechanisms between ischemic stroke and end stage renal disease by Shuhong Wang, Zhongda Li, Xiao Wang, Jiexue Zhou, Shandong Meng, Jinyang Zhuang, Yan Zhou, Qin Zhao, Chunli Zhu, Yusheng Zhang, Sheng Shen

“…Protein-protein interaction networks were constructed using STRING with clustering algorithms. …”
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A subregional shallow water bathymetry derivation method for coral reef using ICESat-2 and Sentinel-2 combined with sediment information by Caixiang Xu, Xiaoguang Ruan, Chenhan Shen, Zixin Tao, Xiaohan Xu, Jiangnan Zheng, Weijiang Wu

“…In this study, a subregional shallow water satellite-derived bathymetry method in combination with sediment information (SDB_SS) is proposed by integrating various algorithms. Firstly, based on the Sentinel-2 multi-spectral imagery, the study area is roughly divided into six types of sediment samples: Sand, Rubble, Seagrass, Rock, Microalgae mat, and Coral / Algae. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. …”
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Multi-omics and experimental validation studies reveal key biomarkers of cellular senescence in diabetic retinopathy by Jinju Li, Hao Yang, Yixuan Lin, Zhaohui Fang

“…Subsequent analyses involved constructing protein-protein interaction (PPI) network, cytoHubba screening, enrichment analysis, and immune infiltration analysis. …”
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An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. …”
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Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study by Mengdie Liu, Wen Guo, Jin Peng, Jinhui Wu

“…Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability.ResultsA total of 12 feature variables were identified through the combined use of three machine learning methods. …”
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A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study by Yahan Zhang, Yi Chun, Hongyuan Fu, Wen Jiao, Jizhang Bao, Tao Jiang, Longtao Cui, Xiaojuan Hu, Ji Cui, Xipeng Qiu, Liping Tu, Jiatuo Xu

“…Then, we used 10 different machine learning algorithms to create a predictive model for anemia. …”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…We aimed to comprehensively evaluate the genetic association between inflammation and cervical cancer, and construct an accurate prognosis model based on circulating inflammatory parameters and indexes with machine learning (ML) algorithms.Patients and Methods: We tested the genome-wide association of circulating inflammatory molecules (CIMs) (91 circulating inflammatory cytokines and 10 inflammatory cells) and summary data retrieved from the UK biobank (cases = 1659 and controls =381,902) with two-sample Mendelian randomization (MR) and colocalization analyses. …”
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Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study by Shi Yu Li, Yue Ming Li, Yong Qi Fang, Zhi Ying Jin, Jun Kang Li, Xiao Meng Zou, Si Si Huang, Rui Lan Niu, Nai Qing Fu, Yu Hong Shao, Xuan Tong Gong, Mao Ran Li, Wei Wang, Zhi Li Wang

“…LASSO regression was employed to select radiomic features (RFs), while eight machine learning algorithms were utilized to construct radiomic models based on US, CDFI, and CEUS. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. …”
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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.ResultsAmong the 37 baseline variables, five key predictors were identified: preoperative CRT >400 μm, presence of retinal edema, presence of subretinal fluid (SRF), disorganization of the inner retinal layers (DRIL), and ellipsoid zone (EZ) integrity. …”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. …”
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Identification of SMYD2 as a candidate diagnostic and prognostic biomarker for gastric cancer by Sichao Wang, Chuanxi Zhao, Dongmei Li, Qingzhi Liu, Cuiping Mao, Shanshan Ding, Shujun Zhang, Wenjing Shang

“…Various machine learning methods were employed to construct an HMEs-based diagnostic model for GC. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate model performance. …”
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Long-term prognosis of 47 pediatric patients with Blau syndrome in China by Xinwei Shi, Jianghong Deng, Junmei Zhang, Xiaozhen Zhao, Yinan Zhao, Li Li, Fengqiao Gao, Weiying Kuang, Jiang Wang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

“…A Bayesian network was constructed to integrate prediction algorithms of genetic mutations and clinical manifestations, exploring the complex relationship between genotype and phenotype through R (Version 4.4.1, R Core Development Team). …”
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Expression Characteristics and Prognostic Value of KLRG2 in Endometrial Cancer: A Comprehensive Analysis Based on Multi-Omics Data by Xiaoyan Huang, Ailian Li, Dianbo Xu

“…Prognostic models were constructed via Cox regression and time-dependent ROC analysis. …”
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Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…A total of 962 radiomic features were extracted for each VOI, followed by feature selection and modeling (Rad_VOIT, Rad_VOIPE, and Rad_VOIT + PE models) using machine learning algorithms. A nomogram was developed to integrate significant clinical factors and radiomic predictors for p53 status prediction. …”
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