A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients by Meng Wang, Wei Li, Fang Zhou, Zheng Wang, Xiaoteng Jia, Xingpeng Han

“…Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. …”
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Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…The Lasso, Cox, and Step machine learning algorithms were used to construct and screen the optimal risk assessment prognostic model. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…In addition, seven key genes, including PDK4, S100A8, HTRA1, CHI3L1, WT1, CDKN1B, and EGF, were screened by machine learning algorithm. Most of these genes exhibited low expression in renal tissue of DN patients and positive correlation with neutrophils, and their expressions were verified in an external dataset cell model. …”
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GPR65 is a novel immune biomarker and regulates the immune microenvironment in lung adenocarcinoma by Hanxu Zhou, Zhi Chen, Shuang Gao, Chaoqun Lian, Junjie Hu, Jin Lu, Lei Zhang

“…We screened differential genes (DEGs) in the immune and stromal components, and then screened modular genes by the WGCNA algorithm, which were intersected with DEGs and incorporated into the LASSO-COX regression model. …”
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InvarNet: Molecular property prediction via rotation invariant graph neural networks by Danyan Chen, Gaoxiang Duan, Dengbao Miao, Xiaoying Zheng, Yongxin Zhu

“…Predicting molecular properties is crucial in drug synthesis and screening, but traditional molecular dynamics methods are time-consuming and costly. …”
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Multi-Omics and Experimental Validation Identify GPX7 and Glutathione-Associated Oxidative Stress as Potential Biomarkers in Ischemic Stroke by Tianzhi Li, Sijie Zhang, Jinshan He, Hongyan Li, Jingsong Kang

“…Multidimensional feature screening using unsupervised consensus clustering and a series of machine learning algorithms led to the identification of the signature gene <i>GPX7</i>. …”
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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning by Nanding Li, Naoshi Kondo, Yuichi Ogawa, Keiichiro Shiraga, Mizuki Shibasaki, Daniele Pinna, Moriyuki Fukushima, Shinichi Nagaoka, Tateshi Fujiura, Xuehong De, Tetsuhito Suzuki

“…This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. …”
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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning by V. Vigna, T. F. G. G. Cova, A. A. C. C. Pais, E. Sicilia

“…The model is efficient, fast, and resource-light, using decision tree-based algorithms that provide interpretable results. …”
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The impact of a coach-guided personalized depression risk communication program on the risk of major depressive episode: study protocol for a randomized controlled trial by JianLi Wang, Cindy Feng, Mohammad Hajizadeh, Alain Lesage

“…Built upon the research on risk prediction modeling and risk communication, we developed a coach-guided, personalized depression risk communication tool (PDRC) for sharing information about individualized depression risk and evidence-based self-help strategies. …”
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Utilizing Multi-omics analysis to elucidate the role of mitochondrial gene defects in Gastric cancer progression. by Jie Chu, Hanying Song, Kemin Fu, Wei Xiao, Jiudong Jiang, Qixin Gan, Bo Deng

“…Additionally, both the ssGSEA algorithm and the CIBERSORT algorithm were utilized to evaluate changes and effects in immunological characteristics during gastric cancer pathogenesis.…”
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Accuracy and interpretability of smartwatch electrocardiogram for early detection of atrial fibrillation: A systematic review and meta‐analysis by Dr. Muhammad Iqhrammullah, Prof. Asnawi Abdullah, Dr. Hermansyah, Fahmi Ichwansyah, Prof. Dr. Ir. Hafnidar A. Rani, Meulu Alina, Artha M. T. Simanjuntak, Derren D. C. H. Rampengan, dr. Seba Talat Al‐Gunaid, dr. Naufal Gusti, dr. Arditya Damarkusuma, Edza Aria Wikurendra

“…Methods Data derived from indexed literature in the Scopus, Scilit, PubMed, Google Scholar, Web of Science, IEEE, and Cochrane Library databases (as of June 1, 2024) were systematically screened and extracted. The quantitative synthesis was performed using a two‐level mixed‐effects logistic regression model, as well as a proportional analysis with Freeman‐Tukey double transformation on a restricted maximum‐likelihood model. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
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WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer by Jing Lv, Jing Lv, Yuhua Zhou, Yuhua Zhou, Shengkai Jin, Shengkai Jin, Chaowei Fu, Chaowei Fu, Yang Shen, Yang Shen, Bo Liu, Bo Liu, Menglu Li, Yuwei Zhang, Yuwei Zhang, Ninghan Feng, Ninghan Feng, Ninghan Feng

“…Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. …”
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Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. by Patrick Y Sips, Xu Shi, Gabriel Musso, Anjali K Nath, Yanbin Zhao, Jason Nielson, Jordan Morningstar, Amy E Kelly, Brittney Mikell, Eva Buys, Vikhyat Bebarta, Jared Rutter, V Jo Davisson, Sari Mahon, Matthew Brenner, Gerry R Boss, Randall T Peterson, Robert E Gerszten, Calum A MacRae

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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
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Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
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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

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
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