Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Univariate Cox regression analysis and Pearson correlation were used to screen for AMD1-related genes (ARGs). Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. …”
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning. by Yinghao Ren, Weiqiang Chen, Yuhao Lin, Zeyu Wang, Weiliang Wang

“…Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”
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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. …”
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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

“…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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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

“…Specifically, the proposed method demonstrates significant advantages on the screened dataset, achieving an AUC of 0.992. At a detection rate of 0.871, it maintains a false alarm rate of 0.9<italic>e</italic>-5, outperforming all comparison algorithms. …”
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Mesangial cell-derived CircRNAs in chronic glomerulonephritis: RNA sequencing and bioinformatics analysis by Ji Hui Fan, Xiao Min Li

“…Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. …”
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Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation by Yuchen Cai, Tianyi Zhou, Xueyao Cai, Wenjun Shi, Hao Sun, Yao Fu

“…Hub genes were further screened and validated by multiple machine learning (ML) algorithms, followed by a comprehensive visualization of single-cell atlas and immune landscape. …”
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Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients by Wenchao Zhou, Hui Li, Juan Zhang, Changsheng Liu, Dan Liu, Xupeng Chen, Jing Ouyang, Tian Zeng, Shuang Peng, Fan Ouyang, Yunzhu Long, Yukun Li

“…Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. …”
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Exploring Mechanisms of Lang Qing Ata in Non-Alcoholic Steatohepatitis Based on Metabolomics, Network Pharmacological Analysis, and Experimental Validation by Li S, Zhu H, Zhai Q, Hou Y, Yang Y, Lan H, Jiang M, Xuan J

“…These discoveries were further validated in subsequent mouse models. An HFHC-induced NASH mouse model was used to validate the therapeutic effects and potential mechanisms of LQAtta on NASH.Results: From the UHPLC-MS/MS analysis of LQAtta, a total of 1518 chemical components were identified, with 106 of them being absorbed into the bloodstream. …”
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Microarray profile of circular RNAs identifies CBT15_circR_28491 and T helper cells as new regulators for deep vein thrombosis by Weiwei Chen, Ying Zhu, Sihua Niu, Yan Zhou, Jian Chang, Shujie Gan

“…Finally, a DVT rat model was established to verify the expression of critical circRNAs and hub genes using real-time quantitative PCR.ResultsA total of 421 circRNAs and 1,082 mRNAs were differentially expressed in DVT. …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. …”
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Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Optimization of Coulomb energies in gigantic configurational spaces of multi-element ionic crystals by Konstantin Köster, Tobias Binninger, Payam Kaghazchi

“…Coulomb energies of possible configurations generally show a satisfactory correlation to computed energies at higher levels of theory and thus allow to screen for minimum-energy structures. Employing an expansion into a binary optimization problem, we obtain an efficient Coulomb energy optimizer using Monte Carlo and Genetic Algorithms. …”
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Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence by Yushuang Dong, HuiPing Liao, Feiming Huang, YuSheng Bao, Wei Guo, Zhen Tan

“…DNA methylation data from 696 newly diagnosed and 194 relapsed pediatric AML patients were analyzed. Feature selection algorithms, including Boruta, least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection, were employed to screen and rank methylation sites strongly correlated with AML recurrence. …”
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Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis by Chuang Yang, Yi-Hang Liu, Hai-Kuo Zheng

“…This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. …”
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