Differential neuropilin isoform expressions highlight plasticity in macrophages in the heterogenous TME through in-silico profiling by Hyun-Jee Han, Marcos Rubio-Alarcon, Thomas Allen, Sunwoo Lee, Taufiq Rahman

“…IntroductionThe nuanced roles of neuropilin (NRP) isoforms, NRP1 and NRP2, have attracted considerable scientific interest regarding cancer progression. …”
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Cathepsin C correlates with M2 macrophage infiltration and regulates the tumor growth and metastasis in non-small cell lung cancer by Xiaoxia Tong, Ting Zhu, Li Ma, Xiaohu Yang, Chenghui Li, Yibing Liu, Xuan Qin, Yanguang Ding, Hongwei Xia, Yonglei Liu

“…Gene set enrichment analysis (GSEA) demonstrated the involvement of CTSC in the immune responses and ssGSEA, CIBERSORT-abs, QUANTISEQ, XCELL algorithms results showed CTSC was positively associated with the M2 macrophages infiltration. …”
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Disseminated intravascular coagulation by Satoshi Gando, Marcel Levi, Cheng-Hock Toh

“…Cell death, damage-associated molecular patterns (including histones), crosstalk between hypoxic inflammation and coagulation, and the serine protease network (comprising coagulation and fibrinolysis, the Kallikrein–Kinin system, and complement pathways) play major roles in DIC pathogenesis. …”
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Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis by Kun Huang, Feng Wen, Jingyi Li, Wenhao Niu, Hui Chen, Shilei Wan, Fupeng Yang, Yihong Chen, Chun Liang

“…A total of 276 intersecting genes were markedly associated with AMI in the pink and turquoise modules. …”
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Identification and experimental validation of BMX as a crucial PANoptosis‑related gene for immune response in Spinal Cord Injury. by Tianbao Feng, Jiating Hu, Mi Xie, Guodong Shi, Qi Wang, Jingyuan Yao, Xiaoqin Liu

“…Research has demonstrated the significant roles of apoptosis, necroptosis, and pyroptosis in the progression of SCI. …”
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Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation by Lina Zhang, Jianjun Gu, Yan Jiang, Juan Xue, Ye Zhu

“…This study aimed to identify hub genes associated with anoikis that may offer therapeutic targets for HF. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“… G protein-coupled receptors (GPCRs) represent one of the most significant classes of drug targets due to their pivotal roles in various physiological processes and disease mechanisms. …”
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Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses by Xindi Chang, Liyu Tao, Lulu Tian, Yingli Zhao, Wangkang Niku, Wang Zheng, Ping Liu, Yiru Wang

“…Based on RNA-seq datasets from the Gene Expression Omnibus database, machine learning algorithms including LASSO, RF, and SVM-RFE were applied. …”
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Exploring hypoxia-related genes in spinal cord injury: a pathway to new therapeutic targets by Shihuan Cheng, Le Li, Mengmeng Xu, Ningyi Ma, Yinhua Zheng

“…These biomarkers were significantly associated with SCI pathogenesis. GO and KEGG analyses highlighted their roles in hypoxia responses, particularly through the hypoxia-inducible factor 1 pathway. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Objective:</b> This study employed multiple machine learning algorithms to analyze the transcriptomes of keratoconus patients, identifying feature gene combinations and their functional associations, with the aim of enhancing the understanding of keratoconus pathogenesis. …”
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Classification of differentially activated groups of fibroblasts using morphodynamic and motile features by Minwoo Kang, Chanhong Min, Somayadineshraj Devarasou, Jennifer H. Shin

“…Fibroblasts play essential roles in cancer progression, exhibiting activation states that can either promote or inhibit tumor growth. …”
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miRNA in Machine-Learning-Based Diagnostics of Oral Cancer by Xinghang Li, Valentina L. Kouznetsova, Igor F. Tsigelny

“…Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. …”
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Nanoscale frontiers in cancer diagnosis and therapy by Tamer A. Addissouky

“…The review also addresses emerging ethical and technical challenges, including data privacy and algorithmic transparency, as nanomedicine moves toward clinical reality. …”
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Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics by Wei Chen, Wei Chen, Xiaofang Wang, Xiaofang Wang, Guoshun Huang, Guoshun Huang, Qin Sheng, Enchao Zhou, Enchao Zhou

“…Through differential gene analysis, Weighted Gene Go-expression Network Analysis (WGCNA) and machine learning algorithms, hub cellular senescence-related differentially expressed genes (CS-DEGs) were identified. …”
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Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis Based on Machine Learning and Single-Cell Sequencing Data by Qi X, Cao S, Chen J, Yin X

“…Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. …”
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Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study by Yejing Zhao, Yejing Zhao, Xiang Wang, Jie Zhang, Yanyan Zhao, Yi Li, Ji Shen, Ying Yuan, Jing Li

“…Functional enrichment analysis showed that fibroblast growth factor 2(FGF2) and yes-associated protein 1(YAP1) protein levels were highly expressed in AD samples, indicating their potential regulatory roles in AD. …”
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Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach by Huali Jiang, Weijie Chen, Benfa Chen, Tao Feng, Heng Li, Dan Li, Shanhua Wang, Weijie Li

“…Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. …”
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Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks by James C. L. Chow, Kay Li

“…These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…However, the combined effects of multiple metals and the modulatory roles of demographic variables remain insufficiently explored.MethodsThis study analyzed data from four NHANES cycles (1999–2000, 2001–2002, 2011–2012, 2013–2014), comprising 1,230 participants aged ≥ 60 years. …”
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