Advancing clinical biochemistry: addressing gaps and driving future innovations by Haiou Cao, Enwa Felix Oghenemaro, Amaliya Latypova, Amaliya Latypova, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Gaffar Sarwar Zaman, Anita Devi

“…However, concerns regarding algorithmic bias, data privacy, lack of transparency in decision-making (“black box” models), and over-reliance on automated systems pose significant challenges that must be addressed for responsible AI integration. …”
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Collaborative Filtering Techniques for Predicting Web Service QoS Values in Static and Dynamic Environments: A Systematic and Thorough Analysis by Ghizlane Khababa, Sadik Bessou, Fateh Seghir, Nor Hazlyna Harun, Abdulaziz S. Almazyad, Pradeep Jangir, Ali Wagdy Mohamed

“…Key insights were gathered on algorithms, evaluation metrics, datasets, and performance outcomes, with a focus on CF methods and advancements in hybrid and context-aware models. …”
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Epidemiological and pharmacoeconomic aspects of HIV infection in military by Yu. I. Bulankov, M. A. Bulygin, A. V. Bespalov, K. V. Zhdanov, A. A. Murachev, K. S. Ivanov, Yu. I. Lyashenko

“…The research compares two competitive models: “Current Situation” - without the introduction of algorithms for early detection and treatment of HIV infection, and “Prognosis” - with the introduction of these algorithms.The following results are obtained: implementing a mandatory triennial screening for HIV-antibodies among military personnel allows to raise the detection of HIV-positive military personnel in the early stages of the disease by 55%. …”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. …”
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Enrollment and Retention Outcomes from the Veterans Health Administration for a Remote Digital Health Study: Multisite Observational Study by Jaclyn A Pagliaro, Lauren K Wash, Ka Ly, Jenny Mathew, Alison Leibowitz, Ryan Cabrera, Jolie B Wormwood, Varsha G Vimalananda

“…ResultsOf the 7714 who were mailed a study invitation, 560 were screened. Of the screened patients, 203 were enrolled (2.9% enrollment yield) and 166 completed the study (82% retention rate). …”
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Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration by Jinquan Chen, Zhao Long, Dandan Shi, Qian Zhang, H. Peng

“…Consequently, the macular was selected as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. …”
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Uncovering Hippo pathway-related biomarkers in acute myocardial infarction via scRNA-seq binding transcriptomics by Xingda Li, Xueqi He, Yu Zhang, Xinyuan Hao, Anqi Xiong, Jiayu Huang, Biying Jiang, Zaiyu Tong, Haiyan Huang, Lian Yi, Wenjia Chen

“…Three machine-learning algorithms prioritized five biomarkers (NAMPT, CXCL1, CREM, GIMAP6, and GIMAP7), validated through multi-dataset analyses and cellular expression profiling. qRT-PCR and Western blot confirmed differential expression patterns between AMI and controls across experimental models. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. …”
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Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches by Xinmin Jin, Lei Li, Xiaoyan Hu, Pengfei Bi, Song Zhang, Qian Wang, Zhongwei Xiao, Hua Yang, Tongtong Liu, Lifang Feng, Jinhuan Wang

“…Objectives: This study aimed to explore the association between urinary metal element levels and sarcopenia across different glucose metabolic states using multi-omics clustering algorithms and machine learning models, and to identify diagnostic biomarkers. …”
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Transforming heart transplantation care with multi-omics insights by Zhengbang Zou, Jianing Han, Zhiyuan Zhu, Shanshan Zheng, Xinhe Xu, Sheng Liu

“…Single–cell omics technologies and machine learning algorithms further resolve cellular heterogeneity and improve predictive modeling, thereby enhancing the clinical translatability of multi-omics data. …”
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Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics by Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu

“…The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74–0.66 across TCGA, CGGA and GEO). …”
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The microRNA expression signature of CD4+ T cells in the transition of brucellosis into chronicity. by Ferah Budak, Salih Haldun Bal, Gulcin Tezcan, Emin Halis Akalın, Abdullah Yılmaz, Pınar Hız, Haluk Barbaros Oral

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Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data by Maomao Guo, Sudong Liang, Zhenghui Guan, Jingcheng Mao, Zhibin Xu, Wenchao Zhao, Hao Bian, Jianfeng Zhu, Jiangping Wang, Xin Jin, Yuan Xia

“…In this study, we utilized bioinformatics and machine learning techniques to analyze public datasets and validated our findings using clinical specimens from our center to identify common signature genes between PCa and MS. We began by screening differentially expressed genes (DEGs) and module genes through Linear models for microarray analysis (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) of four microarray datasets from the GEO database (PCa: GSE8511, GSE32571, and GSE104749; MS: GSE98895). …”
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Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus by Changfan Peng, Long Hu, Wanwen Su, Xin Hu

“…Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. …”
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The Role of AI in Nursing Education and Practice: Umbrella Review by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Fuad H Abuadas, Joel Somerville

“…First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. …”
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Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity by David Melvin Braga, Bharat Rawal

“…In this context, “in silico” describes scientific studies performed using computer algorithms, simulations, or digital models to analyze biological, chemical, or physical processes without the need for laboratory (in vitro) or live (in vivo) experiments. …”
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Cer(d18:1/16:0) as a biomarkers for acute coronary syndrome in Chinese populations by Liang Zhang, Yang Zhang, YaoDong Ding, Tong Jin, Yi Song, Lin Li, XiaoFang Wang, Yong Zeng

“…The area under the ROC curve was used to screen the most valuable predictor. Distinctive ACS-related variables were screened out using Boruta and LASSO regression. …”
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Shared and Distinctive Inflammation-Related Protein Profiling in Idiopathic Inflammatory Myopathy with/without Anti-MDA5 Autoantibodies by Zhang Y, Hu W, Li T, Pan Z, Sun J, He Y, Guan W, Zhang L, Lian C, Liu S, Zhang P

“…The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to anti-MDA5+ DM.Results: Compared with HCs, 36 inflammation-related proteins were identified as DEPs. …”
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Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant by Bang-Cheng Tang, Hai-Yan Fu, Qiao-Bo Yin, Zeng-Yan Zhou, Wei Shi, Lu Xu, Yuan-Bin She

“…The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. …”
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