Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus by Ji Yang, Yikun Zhou, Jiarui Zhang, Yongqin Zheng, Jundong He

“…Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. …”
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Advancements in the application of artificial intelligence in the field of colorectal cancer by Mengying Zhu, Mengying Zhu, Zhenzhu Zhai, Yue Wang, Fang Chen, Ruibin Liu, Ruibin Liu, Xiaoquan Yang, Guohua Zhao

“…In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…Existing GPM techniques often depend on parametric models, which may fail to capture the intricate patterns of groundwater distribution or adapt to varying data complexities. …”
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Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms by Zhi-Chuan He, Zheng-Zheng Song, Zhe Wu, Peng-Fei Lin, Xin-Xing Wang

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

“…By harnessing these technologies, we can analyze data to gain insights into symptoms and patterns, ultimately facilitating remote patient care. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. …”
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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

“…Candidate genes were derived from intersecting initial DEGs with subtype-associated DEGs. 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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Artificial Intelligence and Ethical Dimensions of Automated Traffic Enforcement: Implications for Public Health, Healthcare Equity, and Social Justice by Patricia Haley

“…The analysis reveals how machine learning and predictive analytics in automated enforcement create disproportionate burdens on marginalized populations through three specific mechanisms: (1) biased algorithmic design that targets low-income neighborhoods more intensively, (2) punitive traffic fine structures that impose greater relative financial hardship on economically disadvantaged families, and (3) opaque implementation practices that limit community understanding and participation. …”
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Unraveling anoikis in glioblastoma: insights from single-cell sequencing and prognostic modeling by Qikai Tang, Chenfeng Ma, Jiaheng Xie, Qixiang Zhang, Bingtao Zhang, Weiqi Bian, Qingyu Lu, Zeyu Wan, Wei Wu

“…We identified anoikis-related genes, constructed a prognostic model using 101 machine learning algorithms, and validated its clinical utility across multiple cohorts.Finally, we also verified the expression of model genes and the function of key gene in clinical samples and cell lines. …”
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Exploring the Current Applications and Future Implications of Artificial Intelligence in Public Health Dentistry: A Narrative Review by Tanushri Mahendra Dalvi, Rajeshri Kotwadekar, Shrivardhan R Kalghatgi, Priyanka Paul Madhu, Priyanka Methildeotare

“…AI applications can also be utilised in epidemiologic surveillance for analysis of health-related information in forecasting disease patterns and drafting evidence based public health policies. …”
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Digital remote monitoring of people with multiple sclerosis by Michelangelo Dini, Michelangelo Dini, Giancarlo Comi, Letizia Leocani, Letizia Leocani, Letizia Leocani

“…We focus on tools and techniques applied to data from wearable sensors, smartphones, and other connected devices, as well as AI-based methods for the analysis of big data.ResultsWearable sensors and machine learning algorithms show significant promise in monitoring motor symptoms, such as fall risk and gait disturbances. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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Identification of key genes and immune infiltration mechanisms in limb ischemia-reperfusion injury: a bioinformatics and experimental study by Qiyun Shi, Taotao Tian, Yanfeng Li

“…Immune cell infiltration patterns were analyzed via CIBERSORT.ResultsFrom 169 differentially expressed genes (116 upregulated, 53 downregulated), machine learning identified four key genes (WNT5A, PLCG, ITPR1, CAMK2A), significantly upregulated in experimental limb IRI (P<0.01). …”
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Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California by Jason G. Su, Eahsan Shahriary, Emma Sage, John Jacobsen, Katherine Park, Arash Mohegh

“…These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. …”
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Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

“…In this study, a high-accuracy R/S model was constructed using the AutoGluon framework and traditional machine learning (ML) algorithms with 1,367 R/S samples of grassland in China, integrating climate, soil, terrain and spectral features. …”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. …”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…Twenty-seven drug-naïve FSZ, 25 CHR, and 28 healthy controls (HCs) were recruited for eye-tracking tasks and resting-state functional magnetic resonance imaging to evaluate eye movement and regional homogeneity (ReHo) differences. Machine-learning algorithms were used to differentiate FSZ from CHR. …”
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Novel multi-omics analysis revealing metabolic heterogeneity of breast cancer cell and subsequent development of associated prognostic signature by Peng Zhang, Cuicui Li, Fen Li, Jiezhong Wu, Kunpeng Hu, He Huang

“…A metabolic risk signature was constructed using machine learning algorithms. Immune cell infiltration and immune checkpoint profiles were assessed to explore tumor microenvironment differences. …”
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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…The AIDPI model was developed and refined using univariate Cox regression analysis and an ensemble of machine learning algorithms. Functional analysis, immunoprofiling, and the role of the MFAP4 gene were investigated to elucidate the biological mechanisms underlying the model. …”
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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

“…At the outset, we conducted differential expression analysis, weighted gene co-expression network analysis (WGCNA), and utilized the cytoHubba plugin, complemented by two machine learning algorithms, receiver operating characteristic (ROC) analysis, and expression level evaluation to identify biomarkers for FS. …”
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