Research on Ship Main Engine Fuel Consumption Model With Data Integration and Noise Cleaning by Yingbin Chen, Zhenping Huang, Liang Feng

“…The findings demonstrate strong generalization capabilities of the models, as indicated by small standard deviations across validation sets. The data integration and noise cleaning method significantly enhanced data quality, yielding notable performance improvements: average reductions in MAE and MAPE for predicted SOG were 0.06 and 0.536%, respectively, with an R2 increase of 0.044. …”
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Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort s... by Jia-Wei Feng, Lu Zhang, Yu-Xin Yang, Rong-Jie Qin, Shui-Qing Liu, An-Cheng Qin, Yong Jiang

“…Results The final 15-feature model achieved AUCs of 0.91, 0.84, and 0.77 across validation cohorts, improving to 0.96, 0.95, and 0.89 after cohort-specific refitting. …”
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Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions by Despoina P. Kiouri, Georgios C. Batsis, Christos T. Chasapis

“…Despite its great importance, experimental data on protein–protein interactions (PPIs) between these species are sparse due to experimental limitations. <b>Methods:</b> This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. …”
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Development and validation of a nomogram for predicting calcification of arteriovenous access in hemodialysis patients by Xueying Li, Xiaocui Wang, Bonan Yan, Yuanke Zhou, Ling Li, Xiaopeng Huang, Qiqi Wang, Enjie Tang

“…Early risk prediction of vascular calcification facilitates timely clinical interventions to preserve vascular access integrity.Methods A cross-sectional analysis was performed. …”
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A methodological systematic review of validation and performance of sepsis real-time prediction models by Zichen Wang, Wen Wang, Che Sun, Jili Li, Shuangyi Xie, Jiayue Xu, Kang Zou, Yinghui Jin, Siyu Yan, Xuelian Liao, Yan Kang, Craig M. Coopersmith, Xin Sun

“…This study systematically reviewed SRPM performance across validation methods, analyzing 91 studies from multiple databases. …”
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A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids by Qinran Zhang, Huizhong Chi, Yanhua Qi, Rongrong Zhao, Fuzhong Xue, Gang Li, Hao Xue

“…Summary: Traditional glioma diagnostic methods have limitations, while liquid biopsy is a promising non-invasive option. …”
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Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis by Huibo Zhang, Ming Luo, Junwei Feng, Juan Tan, Yan Jiang, Dmitrij Frishman, Yang Liu

“…This study evaluates the relationship between TIL features from primary tumor whole slide images (WSIs) and LNM in LUAD. Methods TILScout was utilized to derive patch-level TIL scores and generate global TIL maps from primary tumor WSIs. …”
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Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models by Sughashini Murugesu, Kristofer Linton-Reid, Emily Braun, Jennifer Barcroft, Nina Cooper, Margaret Pikovsky, Alex Novak, Nina Parker, Catriona Stalder, Maya Al-Memar, Srdjan Saso, Eric O. Aboagye, Tom Bourne

“…Conclusions Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage.…”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…Performance consistency across validation folds confirmed the framework’s robustness and generalizability. …”
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Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights. by Jincan Li, Shengyue Wang

“…Immune microenvironment analysis employed multiple deconvolution methods (ESTIMATE, CIBERSORT, xCell) and pathway analysis (GSVA, GSEA). …”
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Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…Traditional screening methods rely on laboratory tests and specialized assessments, which may not be readily accessible in routine primary care and community settings. …”
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Mitochondrial Pathway Signature (MitoPS) predicts immunotherapy response and reveals NDUFB10 as a key immune regulator in lung adenocarcinoma by Peng Luo, Zhenfa Zhang, Lianmin Zhang, Pengpeng Zhang, Mengzhe Zhang, Jianlan Liu, Zhaokai Zhou

“…Mitochondrial dysfunction plays a crucial role in tumor development and immune evasion, but its regulatory mechanisms in LUAD immune microenvironment remain unclear.Methods We integrated 149 mitochondria-related pathways (1,136 coding proteins) to develop and validate the Mitochondrial Pathway Signature (MitoPS) using machine learning approaches across seven independent LUAD cohorts (n=1,231). …”
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Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma by Denggang Fu, Biyu Zhang, Wenyan Fan, Fanfan Zeng, Fanfan Zeng, Jueping Feng, Xin Wang

“…However, studies on its impact in lung adenocarcinoma (LUAD) are limited.MethodsWe developed a prognostic signature comprising 10 FAM-related genes (GPR115, SOAT2, CDH17, MOGAT2, COL11A1, TCN1, LGR5, SLC34A2, RHOV, and DKK1) using data from LUAD patients in The Cancer Genome Atlas (TCGA). …”
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Identification and external validation of tumor DNA methylation panel for the recurrence risk stratification of stage II colon cancer by Tanwei Yuan, Dominic Edelmann, Víctor Moreno, Elisabeth Georgii, Lisa Barros de Andrade e Sousa, Helena Pelin, Xiaofeng Jiang, Jakob Nikolas Kather, Katrin E. Tagscherer, Wilfried Roth, Melanie Bewerunge-Hudler, Alexander Brobeil, Matthias Kloor, Hendrik Bläker, Hermann Brenner, Michael Hoffmeister

“…We aimed to identify a gene methylation panel capable of stratifying high-risk stage II CC patients for recurrence beyond traditional clinical variables. Methods: Genome-wide tumor tissue DNA methylation data were analyzed from 562 stage II CC patients who underwent surgery in Germany (DACHS study). …”
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Intelligent Environmental Health Risk Assessment System for the Elderly in Cold Regions Based on Artificial Intelligence Integrated Development Environment by Tianheng ZHANG, Yao FU, Jian GAO, Huanran XUE

“…The adoption of individual difference parameterization methods, combined with a multi-level cascade prediction framework system design, significantly enhances the prediction accuracy of health risk probability. …”
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Mesenchymal stem cell-derived exosomes for the treatment of knee osteoarthritis: a systematic review and meta-analysis based on rat model by Zhe Wang, Zihao Hu, Lin Niu, Yongsheng Xu, Yansong Qi

“…A comparative analysis of drug delivery strategies was performed to explore better therapeutic effects.MethodsThe study employed a systematic search of PubMed, Embase, and Web of Science databases for comparative studies on exosome treatments in rat knee OA models with cartilage damage up to July 2024. …”
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Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers by Lothar Häberle, Ramona Erber, Paul Gass, Alexander Hein, Melitta Niklos, Bernhard Volz, Carolin C. Hack, Rüdiger Schulz-Wendtland, Hanna Huebner, Chloë Goossens, Matthias Christgen, Thilo Dörk, Tjoung-Won Park-Simon, Andreas Schneeweiss, Michael Untch, Valentina Nekljudova, Sibylle Loibl, Arndt Hartmann, Matthias W. Beckmann, Peter A. Fasching

“…The most precise model had across-validated area under the curve (AUC) of 0.84, sensitivity of 0.82, and specificity of 0.71. …”
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