Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma by Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, Xuemei Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen

“…This study provides novel insights into the molecular subtypes of HCC through the application of robust clustering algorithms based on multi-omics data. The constructed CMLS serves as a valuable tool for early prognostic prediction and for screening potential drug candidates that may enhance the efficacy of immunotherapy, thereby establishing a foundation for personalized treatment strategies in HCC. …”
Get full text

Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…This study explored the effectiveness of common machine learning algorithms in predicting pasture yield of temperate grasslands utilizing Landsat-8 data and ground sample data and provided the valuable support for long-term historical monitoring of pasture resources. …”
Get full text

Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions by Shuxian Yu, Haiyang Jiang, Jing Xia, Jie Gu, Mengting Chen, Yan Wang, Xiaohong Zhao, Zehua Liao, Puhua Zeng, Tian Xie, Xinbing Sui

“…In addition, a tongue image-based risk prediction model was established by deep learning algorithms. …”
Get full text

FAR1 as a ferroptosis-related biomarker and potential therapeutic target in acute kidney injury: integrated bioinformatics and experimental validation by Hao Duan, Jie Yan, Xingyu Fan, Yijun Du, Xing Zhong, Tianrong Pan, Yue Wang

“…Eight diagnostic biomarkers were selected using multiple algorithms, and their predictive accuracy was validated through ROC curve analysis. …”
Get full text

Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

“…Ultimately, such progress will contribute to more sustainable and reliable energy utilisation in diverse industrial applications.Highlights Comprehensive review of AI and CI applications in centrifugal pump hydraulic optimisation.Analysis of machine learning methods for predictive modelling and optimisation.Insights into integrating intelligent algorithms with CFD for high-dimensional, multi-objective optimisation.Identification of future research directions to enhance precision and efficiency in pump design methodologies.…”
Get full text

Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…Our study, built on the “Danish CareForColon2015 trial (cfc2015)” is aimed at closing this gap, by focusing on the full integration of AI in CCE’s pathway, where image processing steps linked to the detection, localization and characterisation of important findings are carried out autonomously using various AI algorithms. We developed a family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. …”
Get full text

Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning by Ye Wang, Zhen Pan, Huajun Cai, Shoufeng Li, Ying Huang, Jinfu Zhuang, Xing Liu, Guoxian Guan

“…Nine machine learning algorithms were used to create predictive models based on these factors. …”
Get full text

Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure by Rao J, Wang X, Wang Z

“…The intersection of the results from machine learning revealed that SERPINA3, GPAT3, ANPEP, and FCER1G can serve as feature genes and form a diagnostic model with a good predictive capability. Unsupervised consensus clustering algorithms reveal the immune and metabolic subtypes of macrophages. …”
Get full text

Evaluation of the BronchiolitisMAD protocol in the out-of-hospital emergency services of the Community of Madrid by Beatriz Jiménez-Sánchez Carpio, Laura Sánchez-García, Itziar Marsinyach-Ros, Joaquín Antonio Rendo-Murillo, Víctor Quesada-Cubo, Marina Gómez-Morán Quintana, Jorge Lorente-Romero, María Isabel Sánchez-Sáenz, Ana María Cintora-Sanz, Cristina Horrillo-García

“…It provides detailed algorithms and checklists for treatment and transfer decisions. …”
Get full text

A Heuristic Approach to Competitive Facility Location via Multi-View K-Means Clustering with Co-Regularization and Customer Behavior by Thanathorn Phoka, Praeploy Poonprapan, Pornpimon Boriwan

“…An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). …”
Get full text

Quantitative assessment and Kirschner-wire fixation of an isolated sustentaculum tali fracture in a 7-year-old girl—a case report by Fengyong Mao, Lei Ni, Li Ju

“…Surgical management consisted of ORIF utilizing two 1.5 mm K-wires to achieve anatomic reduction. The postoperative protocol included 6 weeks of cast immobilization followed by a structured rehabilitation program. …”
Get full text

Spiked Dirichlet Process Priors for Gaussian Process Models by Terrance Savitsky, Marina Vannucci

“…Our simulation results, in particular, show a reduction in posterior sampling variability and, in turn, enhanced prediction performances. …”
Get full text

The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma by Atsuto Katano

“…Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. …”
Get full text

Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

“…The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. …”
Get full text

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
Get full text

Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer by Chenxi Wang, Xiangqian Zhao, Dawei Wang, Jinyun Wu, Jizhen Lin, Weiwei Huang, Yangkun Shen, Qi Chen

“…This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. …”
Get full text

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture by Oumaima Afif, Leonardo Franceschelli, Eleonora Iaccheri, Simone Trovarello, Alessandra Di Florio Di Renzo, Luigi Ragni, Alessandra Costanzo, Marco Tartagni

“…Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. …”
Get full text

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification by Xiaojuan Cai, Ya Zhou, Jie Ren, Jinrong Wei, Shiyu Lu, Hanbing Gu, Weizhe Xu, Xun Zhu

“…Conclusion: Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN.ConclusionOur developed thyroid nodule AI diagnostic model shows favorable predictive value. …”
Get full text

Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. …”
Get full text

Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…MethodsUsing data from the Korea National Health and Nutrition Examination Survey, 5 ML algorithms were used for the prediction of high ASCVD risk: logistic regression (LR), support vector machine, random forest, extreme gradient boosting, and light gradient boosting models. …”
Get full text