Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction by Enriqueta Felip, Paolo Nuciforo, Elena Garralda, Joan Frigola, Ramon Amat, Garazi Serna, Francesco Grussu, Kinga Bernatowicz, Olivia Prior, Marta Ligero, Christina Zatse, Rodrigo Toledo, Manel Escobar, Raquel Perez-Lopez

“…Subsequently, a pan-cancer CT-radiomic signature predicting inflamed TIME (CT-TIME) was developed and externally validated. Machine learning was employed to select robust radiomic features and predict inflamed TIME. …”
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Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study by Suresh K Bhavnani, Weibin Zhang, Daniel Bao, Mukaila Raji, Veronica Ajewole, Rodney Hunter, Yong-Fang Kuo, Susanne Schmidt, Monique R Pappadis, Elise Smith, Alex Bokov, Timothy Reistetter, Shyam Visweswaran, Brian Downer

“…However, the high degree of systematic missingness requires repeating the analysis as the data become more complete by using our generalizable and scalable machine learning code available on the All of Us workbench.…”
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Early prediction of long COVID-19 syndrome persistence at 12 months after hospitalisation: a prospective observational study from Ukraine by Dmytro Chumachenko, Tetyana Chumachenko, Oleksii Honchar, Tetiana Ashcheulova

“…Logistic regression and machine learning-based binary classification models have been developed to predict the persistence of LCS symptoms at 12 months after discharge.Conclusions Compared with post-COVID-19 patients who have completely recovered by 12 months after hospital discharge, those who have subsequently developed ‘very long’ COVID were characterised by a variety of more pronounced residual predischarge abnormalities that had mostly subsided by 1 month, except for steady differences in the physical symptoms levels. …”
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Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…Recent advancements in ocean sciences, particularly in data assimilation (DA), suggest that machine learning can emulate dynamical models, replace traditional DA steps to expedite processes, or serve as hybrid surrogate models to enhance forecasts. …”
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Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy by Jefferson R Wilson, David Anderson, Stephan Duplessis, Julien Cohen-Adad, David W Cadotte, Nathan Evaniew, Jacques Bouchard, Michael Craig, Abdul Al-Shawwa, Kalum Ost, Steve Casha, W Bradley Jacobs, Saswati Tripathy, Peter Lewkonia, Fred Nicholls, Alex Soroceanu, Ganesh Swamy, Kenneth C Thomas, Michael MH Yang, Nicholas Dea

“…Quantitative MRI (qMRI) metrics were derived above and below maximally compressed cervical levels (MCCLs). Various machine learning (ML) models were trained to predict 6 month neurological deterioration, followed by global and local model interpretation to assess feature importance.Results A total of 49 patients were followed for a maximum of 2 years, contributing 110 6-month data entries. …”
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Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory by vahideh mousavi kakhki, Sanaz Khatabi

“…Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization.…”
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Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments by Yang Li, Yang Li, Jian Liu, Jian Liu, Yue Sun, Yue Sun, Yuedi Hu, Yuedi Hu, Qiao Zhou, Qiao Zhou, Chengzhi Cong, Chengzhi Cong, Yiming Chen, Yiming Chen

“…Differentially expressed genes were identified, and weighted gene correlation network analysis was used to characterize gene association. Using three machine learning techniques, we identified the most important hub genes to develop and evaluate a nomogram diagnostic model. …”
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Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions by Qing Zhu, Daniel J Jacob, Kunxiaojia Yuan, Fa Li, Benjamin R K Runkle, Min Chen, A Anthony Bloom, Benjamin Poulter, James D East, William J Riley, Gavin McNicol, John Worden, Christian Frankenberg, Meghan Halabisky

“…WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). …”
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Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index by Leyi Zhang, Xia Li, Xiuhua Liu, Zhiyang Lian, Guozhuang Zhang, Zuyu Liu, Shuangxian An, Yuexiao Ren, Yile Li, Shangdong Liu

“…Additionally, a SHapley Additive eXplanation (SHAP) interpretable machine learning model was employed to identify the dominant factors and thresholds influencing the EEQ in the TNEP and its sub-regions. …”
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Vegetable Crop Growth Modeling in Digital Twin Platform Based on Large Language Model Inference by ZHAO Chunjiang, LI Jingchen, WU Huarui, YANG Yusen

“…These metrics significantly outperform traditional machine learning approaches, including long short-term memory (LSTM), XGBoost, and LightGBM models. …”
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MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia by Haiyuan Wang, Runlin Peng, Yuanyuan Huang, Liqin Liang, Wei Wang, Baoyuan Zhu, Chenyang Gao, Minxin Guo, Jing Zhou, Hehua Li, Xiaobo Li, Yuping Ning, Fengchun Wu, Kai Wu

“…The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. …”
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Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility by Rosalyn W Sayaman, Masaru Miyano, Eric G Carlson, Parijat Senapati, Arrianna Zirbes, Sundus F Shalabi, Michael E Todhunter, Victoria E Seewaldt, Susan L Neuhausen, Martha R Stampfer, Dustin E Schones, Mark A LaBarge

“…Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. …”
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Regional antimicrobial resistance gene flow among the One Health sectors in China by Yuqing Feng, Xin Lu, Jiayong Zhao, Hongmin Li, Jialiang Xu, Zhenpeng Li, Mengyu Wang, Yao Peng, Tian Tian, Gailing Yuan, Yuan Zhang, Jiaqi Liu, Meihong Zhang, A La Teng Zhu La, Geruo Qu, Yujiao Mu, Wanshen Guo, Yongning Wu, Yuyu Zhang, Dexiang Wang, Yongfei Hu, Biao Kan

“…Finally, we showed that machine learning models based on microbiome profiles were effective in predicting the presence of carbapenem-resistant strains, suggesting a valuable approach for AMR surveillance. …”
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Risk factors affecting polygenic score performance across diverse cohorts by Daniel Hui, Scott Dudek, Krzysztof Kiryluk, Theresa L Walunas, Iftikhar J Kullo, Wei-Qi Wei, Hemant Tiwari, Josh F Peterson, Wendy K Chung, Brittney H Davis, Atlas Khan, Leah C Kottyan, Nita A Limdi, Qiping Feng, Megan J Puckelwartz, Chunhua Weng, Johanna L Smith, Elizabeth W Karlson, Regeneron Genetics Center, Penn Medicine BioBank, Gail P Jarvik, Marylyn D Ritchie

“…Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. …”
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Osteopenia Metabolomic Biomarkers for Early Warning of Osteoporosis by Jie Wang, Dandan Yan, Suna Wang, Aihua Zhao, Xuhong Hou, Xiaojiao Zheng, Jingyi Guo, Li Shen, Yuqian Bao, Wei Jia, Xiangtian Yu, Cheng Hu, Zhenlin Zhang

“…A few metabolites were identified as candidate early-warning biomarkers by machine learning analysis, which could indicate bone loss and provide new prevention guidance for osteoporosis.…”
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Carbon stock dynamics of forest to oil palm plantation conversion for ecosystem rehabilitation planning by D. Frianto, E. Sutrisno, A. Wahyudi, E. Novriyanti, W.C. Adinugroho, A.S. Yunianto, H. Kurniawan, H. Khotimah, A. Windyoningrum, I.W.S. Dharmawan, H.L. Tata, S. Suharti, H.H. Rachmat, E.M. Lim

“…Data analysis was carried out using Classification and Regression Tree, a decision tree algorithm used in machine learning for guided classification. Furthermore, purposive sampling was utilized to gather socioeconomic data, followed by the implementation of a benefit-cost analysis.FINDINGS: The results revealed significant changes in the land cover within the Kepau Jaya specific purpose forest area over a 24-year period, with forested areas and open areas decreasing by 23.15 hectares per year and 16.94 hectares per year respectively, and oil palm plantation areas expanding by 40.10 hectares per year. …”
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Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway by Yifan Cao, Zhifang Zhao, Mingchun Wen, Xin Zhao, Dingyi Zhou, Jingyi Qin, Liu Ouyang, Jingyao Cao

“…In combination with optical imagery data, a total of 521 landslide disaster points were identified. (2) In comparison to individual machine learning models, the Stacking demonstrated superior performance, with prediction capabilities and accuracy that surpassed other models. …”
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Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”
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The impact of war on people with type 2 diabetes in Ukraine: a survey studyResearch in context by Oksana Sulaieva, Viktoriia Yerokhovych, Sergii Zemskov, Iuliia Komisarenko, Vitalii Gurianov, Volodymyr Pankiv, Oleksandr Tovkai, Tetyana Yuzvenko, Violetta Yuzvenko, Andrii Tovkai, Zlatoslava Shaienko, Tetyana Falalyeyeva, Nadiya Skrypnyk, Taras Romaniv, Nadiya Pasyechko, Taras Krytskyy, Solomiia Danyliuk, Andrii Klantsa, Dmytro Krasnienkov, Oleksandr Gurbych, Nazarii Kobyliak

“…Next, the impact of intrinsic and war-related factors on T2D progression was assessed via logistic regression analysis and machine learning tools. Findings: Two years of war experience was associated with significant increase in the median HbA1c from 7.8% (7.0–8.93) to 8.4% (7.4–9.9; p < 0.001), with the highest value occurring in eastern and northern Ukraine. …”
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