Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms by Jiayi Zhang, Zheng Zhang, Chenqing Yang, Qingguang Liu, Tao Song

“…Utilizing the single-cell RNA-sequencing dataset (GSE242889) of HCC, we identified malignant cell subtypes associated with microvascular invasion (MVI), in conjunction with the TCGA dataset, selected a set of MVI-related genes (MRGs). We developed an optimal prognostic model comprising 11 genes (NOP16, YIPF1, HMMR, NDC80, DYNLL1, CDC34, NLN, KHDRBS3, MED8, SLC35G2, RAB3B) based on MVI-related signature genes by integrating single-cell transcriptomic analysis with 101 machine learning algorithms. …”
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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning by Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Subhajit Saha, T. Subhash Chandra Bose

“…The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. …”
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Prediction of physicochemical characteristics of Lemon (Citrus limon cv. Montaji Agrihorti) using Vis-NIR spectroscopy and machine learning model by Jihan Nada Salsabila Erha, Dina Wahyu Indriani, Zaqlul Iqbal, Bambang Susilo, Dimas Firmanda Al Riza

“…Equally important, evaluating the fruit's maturity level is crucial for determining the optimal harvest time. In this study, standardizing measurement on maturity level was conducted through Vis-NIR spectroscopy and machine learning models. …”
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Enhancing Grid Stability Through Physics-Informed Machine Learning Integrated-Model Predictive Control for Electric Vehicle Disturbance Management by Bilal Khan, Zahid Ullah, Giambattista Gruosso

“…To address these challenges, a Physics-Informed Machine Learning (PIML)-enhanced Model Predictive Control (MPC) framework is proposed to learn the stochastic behaviors of the EV-introduced disturbance in the power grid. …”
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A New Prediction Model of Cutterhead Torque in Soil Strata Based on Ultra-Large Section EPB Pipe Jacking Machine by Jianwei Lu, Bo Sun, Qiuming Gong, Tiantian Song, Wei Li, Wenpeng Zhou, Yang Li

“…By employing multiple regression analysis and a Levenberg–Marquardt (L-M) algorithm-based neural network, an optimal prediction model for EPB cutterhead torque has been developed. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…Four machine-learning models—light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), random forest (RF), and adaptive boosting (AdaBoost)—and three deep-learning models—multilayer perceptron (MLP), TabNet, and convolutional neural network (CNN)—were employed for subtype classification and OS prediction. …”
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Machine Learning Prediction of CO₂ Diffusion in Brine: Model Development and Salinity Influence Under Reservoir Conditions by Qaiser Khan, Peyman Pourafshary, Fahimeh Hadavimoghaddam, Reza Khoramian

“…This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. …”
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Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations by Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang, Jerry Lin, Sungduk Yu, Walter M. Hannah, Noah D. Brenowitz, Josh Romero, Michael S. Pritchard

“…A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer‐resolution cloud‐resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. …”
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A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study by Mengqing Ma, Caimei Chen, Dawei Chen, Hao Zhang, Xia Du, Qing Sun, Li Fan, Huiping Kong, Xueting Chen, Changchun Cao, Xin Wan

“…Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation. …”
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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS.…”
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Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants by Jing Li, Meng-yao Zhou, Yang Li, Xue Wu, Xin Li, Xiao-li Xie, Li-jing Xiong

“…Variables were selected and incorporated into multiple machine learning models. Among them, the logistic regression model demonstrated relatively high stability and was ultimately selected for modeling. …”
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Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model by Yonglin Jia, Yi Li, Asim Biswas, Jiayin Pang, Xiaoyan Song, Guang Yang, Zhen’an Hou, Honghai Luo, Xiangwen Xie, Javlonbek Ishchanov, Ji Chen, Juanli Ju, Kadambot H.M. Siddique

“…We analyzed climate change trends in Xinjiang and used machine learning-transfer component analysis to build a transferable coupling model for total nitrogen (TN) and soil organic carbon (SOC) indicators, resulting in a cotton suitability zoning that accounts for climate and soil fertility factors. …”
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Robust machine learning models for calculating the carbon dioxide desublimation point within natural gas mixtures at low temperature conditions by Walid Abdelfattah, Munthar Kadhim Abosaoda, Dharmesh Sur, Menon Soumya V, Prabhat Kumar Sahu, Kamred Udham Singh, R. Sivaranjani, Rohit Chauhan, Siya Singla, Fereydoon Ranjbar

“…While all models demonstrated excellent predictive accuracy, the GPM model provided superior results among black-box tools, exhibiting a mean absolute percentage error (MAPE) of 0.99 %. …”
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Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente, Kleber Trabaquini

“…However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. …”
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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model’s predictive decisions. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. …”
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Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests by Thuy Phuong Nguyen, Phuc Khoa Nguyen, Huu Ngu Nguyen, Thanh Duc Tran, Gia Tung Pham, Thai Hung Le, Dinh Huy Le, Trung Hai Nguyen, Van Binh Nguyen

“…The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. …”
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A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study by Zongjie Wei, Xuesong Bai, Yingjie Xv, Shao-Hao Chen, Siwen Yin, Yang Li, Fajin Lv, Mingzhao Xiao, Yongpeng Xie

“…Abstract Objective To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation. …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…The least absolute shrinkage and selection operator (LASSO) and multifactor logistic regression were used to screen predictors. Subsequently, six machine learning (ML) models were developed and compared to identify the optimal model. …”
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A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

“…Feature variables were selected using the Boruta algorithm, and five machine learning algorithms (logistic regression, random forest, extreme gradient boosting, support vector machine, and lasso-stacking) were employed to construct models. …”
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