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

“…Abstract Purpose The extracellular matrix (ECM) plays a pivotal role in the initiation and progression of hepatocellular carcinoma (HCC) by facilitating the proliferation of HCC cells and enabling resistance to Anoikis. ECM also provide structural support that aids in the invasion of HCC cells, thereby influencing the tumor microenvironment. …”
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Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

“…The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. …”
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Identification of novel biomarkers involved in oral squamous cell carcinoma by whole transcriptome sequencing and bioinformatics analysis by Hongliang Du, Zhenze Wang, Mengyi Qi, Yunqing Pang, Qingling Lin, Dengqi He, Jing Wang

“…Potential biomarkers were then identified using five algorithms from CytoHubba. Biomarkers were validated via univariate Cox regression and Kaplan–Meier (K–M) survival analysis. …”
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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

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Application of Phi (<italic>&#x03A6;</italic>), the Golden Ratio, in Computing: A Systematic Review by M. Akhtaruzzaman, Jamal Uddin Tanvin, Amir Akramin Shafie, Fahim Shahryer, Sachitra Halder

“…This study highlights the versatility of Golden Ratio, as its applications and relationships were found to account for 51% and 49%, respectively, across a total of 51 selected studies from 27 countries spanning six continents. The review also categorizes findings by their specific applications and contexts, providing valuable insights into <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>&#x2019;s impact on mathematics, cryptography, search algorithms, machine learning, artificial intelligence, photonics, natural sciences, system design, power engineering, robotics, and practical human life. …”
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Detection and monitoring of Melampsora spp. Damage in multiclonal poplar plantations coupling biophysical models and Sentinel-2 time series by Carlos Camino, Alexey Valero-Jorge, Erika García Lima, Ramón Álvarez, Pieter S.A. Beck, Flor Álvarez-Taboada

“…The best-performing DM, which combined plant traits and spectral indices, achieved an overall accuracy of 89.5 % (Kappa = 0.78) across three tested ML algorithms. Relative importance analysis highlighted chlorophylls (21 %), carotenoids (16 %), and leaf water content (11 %) as the most critical variables for rust detection. …”
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Initial production prediction for horizontal wells in tight sandstone gas reservoirs based on data-driven methods by Jian Sun, Jianwen Gao, Kang Tang, Long Ren, Yanjun Zhang, Zhipeng Miao, Zhe Zhang

“…Six machine learning algorithms were utilized to establish models, and the relevant calculation indicators of different models are compared. …”
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Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests by E. J. Gustafson, D. R. Bronson, M. A. Windmuller-Campione, R. A. Slesak, D. M. Donner

“…Simulating lowland forest dynamics in landscape models has been problematic because lowland hydrology is extremely complex, making most hydrology algorithms intractable at the landscape scale. A succession extension (PnET-Succession) of the LANDIS-II forest landscape model was recently updated to include simple algorithms to approximate lowland hydrology, but estimating parameters of tree species' waterlogging tolerance is difficult. …”
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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. …”
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Identification and analysis of the endoplasmic reticulum stress hub genes in sepsis-associated ARDS by Ling Gao, Tingting Liu, Xiaoyan Li

“…The aim of this study was to identify and analyze hub genes related to ERS in sepsis-associated ARDS using bioinformatics and machine learning algorithms, which may serve as diagnostic markers and therapeutic targets. …”
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Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery by Arezoo Abasi, Ahmad Nazari, Azar Moezy, Seyed Ali Fatemi Aghda

“…Machine learning (ML) algorithms offer promising capabilities in uncovering complex patterns within this data, allowing for more accurate injury risk assessment. …”
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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. …”
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Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members by Nataly Buslón, Davide Cirillo, Oriol Rios, Simón Perera del Rosario

“…Participants expressed apprehensions about potential misdiagnoses or inappropriate treatments due to cisnormative data models. However, they also identified opportunities for AI to enhance health care outcomes, advocating for community-led data collection initiatives and improved algorithmic transparency. …”
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Development and Validation of a Questionnaire for the Assessment of Diabetic Peripheral Neuropathy: Implications for Clinical Practice in Physical Therapy and Patient Management by Constantin CIUCUREL, Mariana TUDOR, Elena Ioana ICONARU

“…Introduction: The aim of this study is to develop and validate a questionnaire that evaluates the impact of diabetic peripheral neuropathy (DPN) on quality of life and functional capacity in older adults, while also exploring correlations between clinical factors and DPN severity to guide personalized treatment strategies. …”
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Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover by Rongpeng He, Jihua Meng, Yanfei Du, Zhenxin Lin, Xinyan You, Xinyu Gao

“…Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. …”
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Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices by Chao Ping Wu, Rachel Benish Shirley, Alex Milinovich, Kaiyin Liu, Eduardo Mireles-Cabodevila, Hassan Khouli, Abhijit Duggal, Anirban Bhattacharyya

“…The LightGBM model outperformed other algorithms, achieving a AUROC of 0.91 (95%CI 0.9–0.91) and performance was held in the 2020 validation cohort (n = 509) with an AUROC of 0.85 (95%CI 0.84–0.85). …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…Various machine learning algorithms can be employed to address this issue. Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Promising methods of prenatal diagnostics based on passive sensors and machine learning by A. A. Ivshin, V. M. Vorobyova, N. A. Malyshev

“…The main problem is generation of effective data processing algorithms for their accurate and unambiguous interpretation. …”
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Energy saving in rail transport by Jan Antoš

“…In these cases they will be substituted by various algorithms, that can be machine-processed and that are part of the simulation calculations. …”
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