The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…Objective: In this research, our goal is to develop a novel framework to mitigate prediction biases in LA caused by sample limitations and data heterogeneity. …”
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Identification of ageing-associated gene signatures in heart failure with preserved ejection fraction by integrated bioinformatics analysis and machine learning by Guoxing Li, Qingju Zhou, Ming Xie, Boying Zhao, Keyu Zhang, Yuan Luo, Lingwen Kong, Diansa Gao, Yongzheng Guo

“…Furthermore, ageing-related DEGs might serve as promising prognostic and predictive biomarkers for HFpEF.…”
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Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies by Zipei Song, Yuheng Wang, Miaolin Zhu, Pengpeng Zhang, Zhihua Li, Xin Geng, Xincen Cao, Jianan Zheng, Jianwei Tang, Liang Chen

“…Compared to other prognostic models, RBS demonstrated superior performance in prognosis prediction. The low-RBS group exhibited a tumor microenvironment (TME) more favorable for efficient immune monitoring and reaction, higher responsiveness to immunotherapy, and a better prognosis. …”
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Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique by Giovana Mancilla Pivato, Gustavo Venâncio da Silva, Beatriz Granetti Peres, Stelio Pacca Loureiro Luna, Monique Danielle Pairis-Garcia, Pedro Henrique Esteves Trindade

“…In refinement, the algorithm with the five best-ranked behaviors had the lowest complexity and predictive capacity equivalent to the algorithm with all behaviors. …”
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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

“…This study presents an explainable machine learning framework for shaft power prediction, utilising real-world Internet of Things (IoT) sensor data collected from nine (9) Very Large Crude Carriers (VLCCs) over a 36-month period. …”
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Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait loci, a... by Guoqing Liu, Xiangwen Lv, Jiahui Qin, Xingqing Long, Miaomiao Zhu, Chuwen Fu, Jian Xie, Peichun He

“…The receiver operating characteristic (ROC) curves indicated that the three genes were valuable for diagnosing AMI in the training and validation sets, and the RT‒qPCR results verified the gene expression data. A prediction model was constructed based on three hub neutrophil coexpressed genes in AMI, and the results revealed good accuracy. …”
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Advancing sustainability: The impact of emerging technologies in agriculture by Ashoka Gamage, Ruchira Gangahagedara, Shyamantha Subasinghe, Jeewan Gamage, Chamini Guruge, Sera Senaratne, Thevin Randika, Chamila Rathnayake, Zammil Hameed, Terrence Madhujith, Othmane Merah

“…The integration of data analytics and machine learning algorithms is transforming supply chain management and enhancing the capabilities of predictive analytics in the context of crop diseases. …”
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Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B by Chenyang Huang, Ying Lu, Ziwei Wang, Qiyu Jiang, Yi Dong, Lili Cao, Jianguo Yan, Zhiqiang Xu, Fuchuan Wang, Yinjie Gao, Junliang Fu, Min Zhang, Fu-Sheng Wang

“…Key clinical indicators, including age, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT), were evaluated for their predictive value in determining disease severity using restricted cubic spline regression models. …”
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Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning by Si Xie, Mo Wu, Yu Shang, Wenbin Tuo, Jun Wang, Qinzhen Cai, Chunhui Yuan, Cong Yao, Yun Xiang

“…Results After variable selection, eight machine learning models were developed using age, sex and 21 serum indicators identified as predictive factors for SMPP. A Light Gradient Boosting Machine (LightGBM) model demonstrated strong performance, achieving AUC of 0.92 for prospective validation. …”
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Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas by Wenshu Li, Zixiang Zhu, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang, Lingchuan Guo

“…Patients in this group exhibited shorter survival periods, potentially attributable to the heightened expression of negative immune checkpoints. Predictive analysis for targeted drugs indicated that certain agents might achieve a lower half maximal inhibitory concentration (IC50) in the low-risk group compared to the high-risk group. …”
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Research on Near-Infrared Non-Invasive Blood Glucose Detection Technology by Wang Jinshuo

“…In response to the complex and easily interfered characteristics of near-infrared spectral signals, an innovative BP neural network algorithm is introduced to construct a prediction model. …”
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Comprehensive Analysis Based on the Cancer Immunotherapy and Immune Activation of Gastric Cancer Patients by Feng Jiang, Qilong Ma

“…Finally, we were able to produce a 6-gene prognostic prediction model using immune-related genes. Further analysis revealed that the prognostic prediction model is closely related to the prognosis of patients with GC. …”
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Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models by Yi Yang

“…The paper explores the subject of data flow optimization using Fuzzy Assisted Cuckoo Search Optimization (FACSO), traffic flow using Gaussian Process Regression (GPR), and CH prediction using the Stochastic Optimization Algorithm (SOA). …”
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Multi-LiDAR-Based 3D Object Detection via Data-Level Fusion Method by Yu Luo, Tao Wang, Shuai Lu, Xuerui Dai, Zhi Li

“…Moreover, we add the direction accuracy by changing the heading angle prediction to interval prediction. Finally, to verify the effectiveness of our method, we propose the public dataset VANJEE Point Cloud, which is collected in the real world. …”
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Application of Neural Network Models for Analyzing the Impact of Flight Speed and Angle of Attack on Flow Parameter Non-Uniformity in a Turbofan Engine Inlet Duct by Adam Kozakiewicz, Maciej Adamczyk, Rafał Kieszek

“…The ANN was trained using the CFG algorithm, and the predictive accuracy was assessed through the determination coefficients computed by comparing ANN-based estimates with numerical simulation results. …”
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Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Additionally, three optimal predictive models (AUC >0.9) were developed and validated for classifying HSIC from HS individuals based on proteomic patterns and machine learning, with the logistic regression model showing the best diagnostic performance (AUC = 0.979, sensitivity = 81.8%, specificity = 96.7%), highlighting lactate dehydrogenase A chain (LDHA), neutrophil gelatinase-associated lipocalin (NGAL), prothrombin and glucan-branching enzyme (GBE) as key predictors of HSIC.ConclusionThe study uncovered critical metabolic and protein changes linked to heatstroke, highlighting the involvement of energy regulation, lipid metabolism, and carbohydrate metabolism. …”
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A model of indoor thermal condition based on traditional acehnese houses using artificial neural network by Muslimsyah, Abdul Munir, Yuwaldi Away, Abdullah, Teuku Yuliar Arif, Andri Novandri

“…The study employs an ANN (Artificial Neural Network) algorithm to predict thermal condition parameters including indoor: temperature, humidity, and wind speed. …”
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AMMap tool for additive manufacturing design, alloy discovery, and path planning by Alexander M Richter, Adam M Krajewski, Zhening Yang, Allison M Beese, Zi-Kui Liu

“…Equilibrium thermodynamic calculations and solidification simulations, such as Scheil–Gulliver, can be used to predict feasible compositions or compositional paths, acting as constraints before empirical or machine learning models are applied to predict properties of interest. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China by Mingxue Xiang, Gang Fu, Jianghao Cheng, Tao Ma, Yunqiao Ma, Kai Zheng, Zhaoqi Wang

“…Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters.…”
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