The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis by Gujun Cong, Jingjing Shao, Feng Xiao, Haixia Zhu, Peipei Kang

“…A diagnostic model was then constructed and validated using a combination of 108 machine learning algorithms. …”
Get full text

High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Rongzi Chai, Ting Zhang, Kaixin Wang, Chaoyu Wang

“…At the algorithmic level, an Auxiliary Attention Temporal Convolutional Network (AuxATTCN) model is proposed, integrating an auxiliary attention mechanism with a Temporal Convolutional Network (TCN) to effectively capture long‐term dependencies and dynamically incorporate external driving factors such as geomagnetic activity and solar radiation. …”
Get full text

Multi-Objective Optimization Methods for University Campus Planning and Design—A Case Study of Dalian University of Technology by Lin Qi, Chaoran Chen, Jun Dong

“…This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. …”
Get full text

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study by Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

“…We used demographic, clinical, and nursing data to construct a prediction model. We constructed unplanned readmission prediction models by dividing them into Model 1 and Model 2. …”
Get full text

Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to identify PCD-related genes. An integrated machine learning framework, combining 12 algorithms was used to construct a PCDS model. …”
Get full text

Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study by Zhenli Li, Jing He, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Mengjia Li, Mengjia Li, Zhengkun Guan, Zhengkun Guan, Ruolian Gao, Jingtao Ma

“…We employed LR and LASSO algorithms to initially screen out 15 and 20 features, respectively. …”
Get full text

Data Interpretation in Structural Health Monitoring: Toward a Universal Language by Magda Ruiz, Óscar Gualdrón, José A. Peral Mondaza, Luis Eduardo Mujica Delgado

“…The findings highlight the potential of context-aware algorithms and integrated data sources to mitigate these biases. …”
Get full text

Adaptive dual-graph learning joint feature selection for EEG emotion recognition by Liangliang Hu, Congming Tan, Yin Tian

“…However, EEG signals exhibit significant variations across different subjects and experimental sessions, posing challenges to the generalization of emotion recognition algorithms to unseen scenarios. To relieve this issue, we designed a novel hybrid EEG emotion recognition model named DGLFS, which integrates domain-invariant feature selection, label propagation, and adaptive dual-graph regularization into a unified optimization framework. …”
Get full text

Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis by Negin Sanaei, Mohamad Zamani-Ahmadmahmudi, Seyed Mahdi Nassiri

“…With the advent of machine learning methods/algorithms, various models can be developed using a combination of clinical and non-clinical variables to predict clinical outcome in different diseases with higher efficiency compared to traditional biomarkers. …”
Get full text

Ecological niche models as a tool for estimating the distribution of plant communities by Mayra Flores-Tolentino, Enrique Ortiz, José Luis Villaseñor

“…It is important to adapt the available information and knowledge about the object of study, to properly integrate them into the different algorithms that allow us to obtain an approximation of what happens with the species or communities. …”
Get full text

Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks by Witwisit Kesornsit, Yaowarat Sirisathitkul

“…This study presents a hybrid machine learning model by integrating dimensionality reduction and feature selection algorithms with a backpropagation neural network (BPNN) to predict electricity consumption in Thailand. …”
Get full text

Predicting the Acceptance of Informal Learning Technologies: A Case of the TikTok Application by Ahmed Al-Azawei, Ali Alowayr

“…Moreover, previous literature focused on the use of structural equation modeling (SEM) to predict technology acceptance, whereas the application of data mining algorithms is rare in this direction of research. This study, therefore, aims to (1) propose an integrated framework based on the DeLone and McLean information system model, the diffusion theory, the interactivity theory, the intrinsic motivation theory, and the security perceptions, (2) predict the adoption of TikTok as a learning means in an informal educational space, and (3) compare the performance of data mining techniques and SEM in predicting users’ behavioral intention towards TikTok acceptance. …”
Get full text

Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

Get full text

Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

“…Using two data preprocessing methods and three feature extraction techniques, we constructed and validated models with eight machine learning algorithms. …”
Get full text

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis by Yuxing Liu, Jintao Chen, Daifeng Yang, Chenming Liu, Chunhui Tang, Shanshan Cai, Yingxuan Huang

“…Abstract This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. …”
Get full text

Machine learning study on magnetic structure of rare earth based magnetic materials by Dan Liu, Jiahe Song, Zhixin Liu, Jine Zhang, Weiqiang Chen, Yinong Yin, Jianfeng Xi, Xinqi Zheng, Jiazheng Hao, Tongyun Zhao, Fengxia Hu, Jirong Sun, Baogen Shen

Get full text

Exploring PID and FOPID Control Methods for Thrust Vectoring in Aerospace Systems-A Beginner’s Guide by Menhal Abdulmohsen Alkodur, Ali Hussain Almusharraf, Ghulam E. Mustafa Abro, Eman Mahmoud, Ayman M. Abdallah

“…The study instructs readers on constructing a physical prototype with SolidWorks software and experimentally evaluating the control algorithms. …”
Get full text

Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

“…In recent years, deep learning has emerged as a promising approach for identifying key nodes in networks. However, existing algorithms fail to effectively integrate local and global structural information, leading to incomplete and limited network understanding. …”
Get full text

The classification method of donkey breeds based on SNPs data and machine learning by Dekui Li, Dekui Li, Xiaolong Hu, Yongdong Peng

“…A method for accurately classifying donkey breeds has been developed by integrating single nucleotide polymorphism (SNPs) data with machine learning algorithms. …”
Get full text

Multi-Level Thresholding Based on Composite Local Contour Shannon Entropy Under Multiscale Multiplication Transform by Xianzhao Li, Yaobin Zou

“…Existing approaches predominantly rely on metaheuristic optimization algorithms, which frequently encounter local optima stagnation and require extensive parameter tuning, thereby degrading segmentation accuracy and computational efficiency. …”
Get full text