Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma by Weiwei Zhu, Weiwei Zhu, Huifen Wang, Huifen Wang, Yudie Cai, Yudie Cai, Jun Lei, Jun Lei, Jia Yu, Jia Yu, Ang Li, Zujiang Yu

“…Diagnostic and prognostic prediction models were formulated using the random forest algorithm, and the performance of these models was rigorously evaluated through receiver operating characteristics curve (ROC) analysis.ResultsThe methylation level of HIST1H3G was markedly elevated in both HCC tissues and plasma samples derived from HCC patients. …”
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Artificial intelligence-based radiogenomics reveals the potential immunoregulatory role of COL22A1 in glioma and its induced autoimmune encephalitis by Bingchao Yan, Bingchao Yan, Qian Chen, Dacheng Wang, Leili Ding, Leili Ding, Jingfeng Qu, Renfei Du, Renfei Du, Wenjie Shi, Ulf D. Kahlert, Zhengquan Yu

“…Immune infiltration was evaluated using five algorithms (MCP-counter, EPIC, TIMER, QUANT and IPS). …”
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Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease by Ruijie Zhao, Yun Wang, Jiaru Wang, Zixing Wang, Ran Xiao, Ying Ming, Sirong Piao, Jinhua Wang, Lan Song, Yinghao Xu, Zhuangfei Ma, Peilin Fan, Xin Sui, Wei Song

“…Conclusion With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. …”
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Microstate Co-Occurrence Matrices-Based Brain Activity Pattern Changes Analysis on Chronic Tinnitus Subjects by Yingying Wang, Gui Yang, Hongyu Liu, Xin Huang, Shuqing Han, Zhixiang Gu, Liyang Shao, Lei Zhang, Yuan Tao

“…Unlike healthy individuals, tinnitus patients exhibit minimal changes between OE and CE, indicating the reduction of brain flexibility. This study advances EEG microstate analysis by integrating co-occurrence matrix algorithms, offering a direct and comprehensive assessment of microstate stability and interactions. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…Conclusions This study developed an optimized mapping algorithm to predict the utility index from the QLQ-C30 QLQ-LC13 to the SF-6D. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Conclusion The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. …”
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Phasic and periodic change of drought under greenhouse effect by Yang Li, Zhicheng Zheng, Yaochen Qin, Haifeng Tian, Zhixiang Xie, Peijun Rong

“…It can lead to crop reduction and even pose a threat to human survival in environmentally sensitive areas of China (ESAC). …”
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Advanced health state intelligent diagnosis of lithium-ion batteries based on CNN-WNN-WBiLSTM model with attention mechanism by Walid Mchara, Mohamed Abdellatif Khalfa, Lazhar Manai

“…Our results underscore the efficacy of our algorithm in Li-ion battery health management, offering higher prediction accuracy and precise multi-step SOH prediction compared to other neural network methods.…”
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An analysis of the relationship of triglyceride glucose index with diffuse large B-cell lymphoma prognosis: a retrospective study by QingQing Luo, Zhixiang Lei, Haizhou Miao, Ting Huang, Li Yu

“…Calibration curves showed good agreement between the nomogram predictions and actual outcomes, and DCA highlighted the high clinical utility of the model.ConclusionThe TyG index is an independent prognostic factor in DLBCL patients, and the TyG-based nomogram model provides enhanced predictive accuracy compared to the IPI. …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”
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AI-driven multi-omics profiling of sepsis immunity in the digestive system by Yuan Gao, Hong Chen, Ruolan Wu, ZuJun Zhou

“…It focuses on the synthesis of clinical biomarkers of sepsis and parameters related to the gut microenvironment with the help of artificial intelligence, enabling marker identification, immunostratification and predictive modeling. This feasible clinical decision-making algorithm based on “combinatorial typing” is an important tool for realizing precision medicine for sepsis patients.…”
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Dynamic Modeling and Composite Attitude Cooperative Control for Dumbbell-Shaped Spacecraft by Qingmu Ai, Yingzi Guan, Yuzhi Wang, Shunli Li

“…Then, leveraging graph and consensus theories, an attitude coordination consistency algorithm is developed. A virtual leader is designed using model predictive control. …”
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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

“…The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…Abstract Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty. …”
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Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search by Yuan Yang, Yiyang Wang, Bowen Xue, Changxu Wang, Bo Yang

“…Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. …”
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Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer by Man Wu, Wanyi Feng, Xinya Li, Yunan Liu, Chuxin Cao

“…Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. …”
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Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressi... by Elizabeth Kresock, Bryan Dawkins, Henry Luttbeg, Yijie Jamie Li, Rayus Kuplicki, B A McKinney

“…<h4>Background</h4>Nearest-neighbor projected-distance regression (NPDR) is a metric-based machine learning feature selection algorithm that uses distances between samples and projected differences between variables to identify variables or features that may interact to affect the prediction of complex outcomes. …”
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ICD-10-AM codes for cirrhosis and related complications: key performance considerations for population and healthcare studies by Kelly L Hayward, Amy L Johnson, Benjamin J Mckillen, Vikas Bansal, Leigh U Horsfall, Gunter Hartel, Chris Moser, Elizabeth E Powell, Patricia C Valery

“…Accuracy of individual codes and grouped combinations was determined by calculating sensitivity, positive predictive value (PPV), negative predictive value and Cohen’s kappa coefficient (κ).Results The PPVs for ‘grouped cirrhosis’ codes (0.96), hepatocellular carcinoma (0.97) ascites (0.97) and ‘grouped varices’ (0.95) were good (κ all &gt;0.60). …”
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HEURISTIC OPTIMIZATION OF ANTENNA DEPLOYMENT FOR COMBINATIONAL TWO-DIMENSIONAL RFID-BASED LOCALIZATION METHOD by Yu. B. Gimpilevich, D. A. Savochkin

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Halftone image compression based on probabilistic run length encoding by H. K. Al-Bahadily, V. Yu. Tsviatkou, V. K. Kanapelka

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