Expression Characteristics and Prognostic Value of KLRG2 in Endometrial Cancer: A Comprehensive Analysis Based on Multi-Omics Data by Xiaoyan Huang, Ailian Li, Dianbo Xu

“…High KLRG2 expression independently predicted worse overall survival (HR = 3.08, 95% CI = 1.92–4.96) and progression-free interval (HR = 1.98, 95% CI = 1.37–2.87). …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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Mechanisms of drug resistance to neoadjuvant chemotherapy in breast cancer by K. A. Aliev, E. Yu. Zyablitskaya, T. P. Makalish, L. E. Sorokina, E. R. Asanova

“…The presented results can serve as the basis for the creation of diagnostic algorithms that have predictive value regarding the effectiveness of NCT, and also to help identify new targets to justify the use of combined breast cancer treatments in the early stage.…”
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The UK Biobank mental health enhancement 2022: Methods and results. by Katrina A S Davis, Jonathan R I Coleman, Mark Adams, Gerome Breen, Na Cai, Helena L Davies, Kelly Davies, Alexandru Dregan, Thalia C Eley, Elaine Fox, Jo Holliday, Christopher Hübel, Ann John, Aliyah S Kassam, Matthew J Kempton, William Lee, Danyang Li, Jared Maina, Rose McCabe, Andrew M McIntosh, Sian Oram, Marcus Richards, Megan Skelton, Fenella Starkey, Abigail R Ter Kuile, Laura M Thornton, Rujia Wang, Zhaoying Yu, Johan Zvrskovec, Matthew Hotopf

“…It includes established measures, such as the PHQ-9 for current depression and CIDI-SF for lifetime panic, as well as bespoke questions. Algorithms and R code were developed to facilitate analysis.…”
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Emerging Healthcare Technologies in Gastrointestinal Endoscopy: 75 Years of Evolution by Irina Florina CHERCIU HARBIYELI, Elena Daniela BURTEA, Vlad IOVANESCU, Dan Nicolae FLORESCU, Ion ROGOVEANU, Dan Ionut GHEONEA, Adrian SAFTOIU

“…One of the most significant advancements is AI-driven endoscopy systems (EndoBrain, NvisionVLE, EndoAngel, GI Genius). Machine learning algorithms assist in real-time polyp detection, classification, and predictive analytics, enhancing diagnostic accuracy while reducing inter-observer variability and human error. …”
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Deployment of real-time particle detection monitoring system in operating theatres for airborne contamination assessments: a methodological evaluation by Frans Stålfelt, Johan Tenghamn, Henrik Malchau, Karin Svensson Malchau

“…Future research should focus on integrating predictive algorithms and machine-learning to enhance clinical utility and drive improvements in surgical safety. …”
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Study on Lacticaseibacillus casei TCS fermentation kinetic models and high-density culture strategy by Chen Chen, Tianyu Guo, Di Wu, Jingyan Shu, Ningwei Huang, Huaixiang Tian, Haiyan Yu, Chang Ge

“…Subsequently, we applied the artificial neural network–genetic algorithm optimization method, which significantly increased the viable bacterial count. …”
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Digital Self-Management Intervention Paths for Early Breast Cancer Patients: Results of a Pilot Study by Paula Poikonen-Saksela, Evangelos Karademas, Leena Vehmanen, Meri Utriainen, Haridimos Kondylakis, Konstadina Kourou, Georgios C. Manikis, Eleni Kolokotroni, Panagiotis Argyropaidas, Berta Sousa, Ruth Pat Horenczyk, Ketti Mazzocco, Johanna Mattson

“…Based on these answers and predictive algorithms for anxiety and depression, they were guided onto one or several digital intervention paths. …”
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Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen shoulder by Han-tao Jiang, Li-ping Shen, Meng-Qi Pang, Min-jiao Wu, Jiang Li, Wei-jie Gong, Gang Jin, Rang-teng Zhu

“…The nomogram constructed based on them had a good predictive value for the occurrence of FS. Except for DCN, the other four genes were upregulated in FS samples, and the expression of SNAI1, TWIST1, and TUBB2B was also observed to be significantly upregulated in RT-qPCR. …”
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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

“…Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Feature selection method impacts radiomics models’ performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. …”
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IoT-driven smart agricultural technology for real-time soil and crop optimization by Hammad Shahab, Muhammad Naeem, Muhammad Iqbal, Muhammad Aqeel, Syed Sajid Ullah

“…By integrating advanced IoT technologies, cloud computing, predictive algorithms, and a smart soil sensor, this system revolutionizes agriculture by enabling real-time monitoring of critical factors influencing rice crops metabolism. …”
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Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates by Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou

“…Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. …”
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Modeling of the Potential Effect of Revaccination against Whooping Cough in Children Aged 6–7 and 14 years within the Framework of the National of preventive vaccinations by N. I. Briko, A. Ya. Mindlina, I. V. Mikheeva, L. D. Popovich, A. V. Lomonosova

“…A simulation dynamic mathematical model is constructed that allows predicting the development of the epidemiological process of whooping cough on the basis of the dynamics of the main indicators of its prevalence in the population that developed in previous years. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. Results Our results demonstrated that high MRE11 expression is strongly associated with poor prognosis in HCC. …”
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The photometry and kinematics studies of NGC 2509 derived from Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…We employed the pyUPMASK Python package and HDBSCAN algorithms to identify the cluster member stars. The current analysis introduces a new method that connects the membership probability of stars in the cluster (using the pyUPMASK tool) with the number of stars predicted by the King model at different distances from the center of the cluster. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.…”
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CELIAC DISEASE SCREENING IN A LARGE DOWN SYNDROME COHORT: COMPARISON OF DIAGNOSTIC YIELD OF DIFFERENT SEROLOGICAL SCREENING TESTS by Dilek Uludağ Alkaya, Seçil Sözen, Birol Öztürk, Nuray Kepil, Tülay Erkan, Hüseyin Tufan Kutlu, Beyhan Tüysüz

“…This study aimed to estimate the prevalence of CD in DS patients and compare the diagnostic performance of the screening algorithms. Material and Method: A cohort of 1117 DS patients were included. …”
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Assessment of Risks of Voltage Quality Decline in Load Nodes of Power Systems by Pylyp Hovorov, Roman Trishch, Romualdas Ginevičius, Vladislavas Petraškevičius, Karel Šuhajda

“…Based on the results of the study, a mathematical model of the risk of voltage collapses in networks, an algorithm and a methodology for its calculation were proposed.…”
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