Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine... by Chenghui Cao, Chenghui Cao, Wenwu Liu, Xin Guo, Shuwei Weng, Yang Chen, Yonghong Luo, Shuai Wang, Botao Zhu, Botao Zhu, Yuxuan Liu, Yuxuan Liu, Daoquan Peng

“…This analysis was followed by a series of in-depth investigations, including protein–protein interaction (PPI), correlation analysis, and functional enrichment analysis, to uncover the molecular interactions and pathways at play. To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. …”
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New insights into biomarkers and risk stratification to predict hepatocellular cancer by Katrina Li, Brandon Mathew, Ethan Saldanha, Puja Ghosh, Adrian R. Krainer, Srinivasan Dasarathy, Hai Huang, Xiyan Xiang, Lopa Mishra

“…Abstract Hepatocellular carcinoma (HCC) is the third major cause of cancer death worldwide, with more than a doubling of incidence over the past two decades in the United States. …”
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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
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Sparse representation for massive MIMO satellite channel based on joint dictionary learning by Qing yang Guan, Shuang Wu

“…Simulation results demonstrate that the proposed CJDL algorithm provides a more accurate and robust channel representation.…”
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Callback time preference for prescreening visits among Black residents in the Boston area: findings from two randomized controlled trials by Ruth Zeto, Oluwagbemisola Ibikunle, Jingyi Cao, Hannah Col, Dhrumil Patil, Ruth-Alma Turkson-Ocran, Mingyu Zhang, Timothy B. Plante, Stephen P. Juraschek

“…Staff call attempts and participant screening status were logged prospectively. Gender was estimated based on first name, using a published algorithm. …”
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Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm by Saantosh Saravanan, N. Aravindha Babu, Lakshmi T, Mukesh Kumar Dharmalingam Jothinathan

“…Specialized algorithms such as the Recombination-Based Improved Population Optimization Parallel Covariance Matrix Adaptation Evolution Strategy (RB-IPOP CMA-ES) allow for better accuracy of deep learning models, as this enhances the performance of developing models for early diagnosis of oral cancer. …”
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Machine Learning–Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study by Pinjie Huang, Jirong Yang, Dizhou Zhao, Taojia Ran, Yuheng Luo, Dong Yang, Xueqin Zheng, Shaoli Zhou, Chaojin Chen

“…ConclusionsWe have developed and validated a generalizable random forest model to predict postoperative early complications in patients undergoing intestinal obstruction surgery, enabling clinicians to screen high-risk patients and implement early individualized interventions. …”
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How we treat polycythemia vera by V. A. Shuvaev, I. S. Martynkevich

“…The discovery of the pathogenetic role of mutations in the Janus kinase II gene has led to the possibility of establishing a diagnosis based not only on morphology, but also on genetic verification and to the development of directed targeted therapy, which is much more effective than previously used methods. The introduction of molecular genetic screening led to the need for a differential diagnosis with familial erythrocytosis, and the lessons of the coronavirus pandemic revealed the presence in the population of a significant proportion of patients with erythrocytosis due to the carriage of gene polymorphisms associated with familial hemochromatosis. …”
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Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis by Yanan Li, Yuqiu Tan, Zengwen Ma, Zengwen Ma, Weiwei Qian, Weiwei Qian

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”
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Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India by Anita Raj, Abhishek Singh, Nandita Bhan, Lotus McDougal, Nabamallika Dehingia, Julian McAuley

“…Analyses included iterative thematic analysis (L-1 regularised regression followed by iterative qualitative thematic coding of L-2 regularised regression results) and neural network modelling.Outcome measure Participants reported their experiences of sexual violence perpetrated by their current (or most recent) husband in the previous 12 months. …”
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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…The statistical resampling approach based on GMM was applied to Sentinel-2 (S2) imagery to produce input to Machine Learning (ML) algorithms to retrieve the TSS and turbidity for target river sections. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Predictive and preventative models should target factors with a high causality index and low investigative costs. …”
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Research on the correlation between retinal vascular parameters and axial length in children using an AI-based fundus image analysis system. by Chaoyang Zhao, Huilin Li, Ziyou Yuan, Zihan Yang, Tiantian Wang, Yan Wang, Qian Tong, Shaofeng Hao

“…The findings aim to provide a scientific basis for the prevention, early screening, and formulation of personalized treatment strategies for myopia.…”
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Exploring pesticide risk in autism via integrative machine learning and network toxicology by Ling Qi, Jingran Yang, Qiao Niu, Jianan Li

“…Each combination of 1–23 targets was used to construct predictive models using eight different machine learning algorithms. …”
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Machine learning-derived prognostic signature integrating programmed cell death and mitochondrial function in renal clear cell carcinoma: identification of PIF1 as a novel target by Guangyang Cheng, Zhaokai Zhou, Shiqi Li, Fu Peng, Shuai Yang, Chuanchuan Ren

“…Finally, a novel RCC prognostic marker PIF1 was identified in model genes. The knockdown of PIF1 in vitro inhibited the progression of renal carcinoma cells. …”
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Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. …”
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Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis by Qiang Shen, Lin Fan, Chen Jiang, Dingyi Yao, Xingyu Qian, Fuqiang Tong, Zhengfeng Fan, Zongtao Liu, Nianguo Dong, Chao Zhang, Jiawei Shi

“…Machine learning (ML) algorithms were employed to screen potential biomarkers. …”
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Signatures of Six Autophagy‐Related Genes as Diagnostic Markers of Thyroid‐Associated Ophthalmopathy and Their Correlation With Immune Infiltration by Qintao Ma, Yuanping Hai, Jie Shen

“…The combined six‐gene model also showed good diagnostic efficacy (AUC = 0.948). …”
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Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma by Pingfei Tang, Weiming Qu, Dajun Wu, Shihua Chen, Minji Liu, Weishun Chen, Qiongjia Ai, Haijuan Tang, Hongbing Zhou

“…Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. …”
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