Integrated multi-omics and machine learning reveal an immunogenic cell death-related signature for prognostic stratification and therapeutic optimization in colorectal cancer by Siyu Hou, Shanshan Heng, Shaozhuo Xie, Yuanchun Zhao, Jiajia Chen, Chunjiang Yu, Yuxin Lin, Yuxin Lin, Xin Qi

“…Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. …”
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A Hybrid Sequential Feature Selection Approach for Identifying New Potential mRNA Biomarkers for Usher Syndrome Using Machine Learning by Rama Krishna Thelagathoti, Wesley A. Tom, Dinesh S. Chandel, Chao Jiang, Gary Krzyzanowski, Appolinaire Olou, M. Rohan Fernando

“…The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. …”
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Integrating environmental and LULC drivers of groundwater droughts in groundwater-dependent ecosystems: a machine learning (XGBoost)-SEM analysis with ecosystem implications by Kawawa Banda, Christopher Shilengwe, Imasiku Nyambe

“…Results The study reveals that LULC types, particularly water bodies, cropland and bare land, exert the greatest influence on groundwater drought responses under teleconnection patterns attributed to ENSO, rather than through changes in the net water balance. …”
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Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…Abstract Background The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. …”
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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

“…Abstract Background This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. …”
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Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Unsupervised learning revealed no distinct distribution patterns between patients with/without CAAs. <b>Conclusions</b>: Despite utilizing a large dataset to develop a machine learning-based prediction model for CAAs, the performance was unsatisfactory. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…Abstract In this paper, three Double Machine Learning (DML) models are proposed to enhance the accuracy of breast cancer detection using machine learning techniques using breast cancer detection dataset. …”
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Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study by Nate C. Carnes, Claire A. Kolaja, Crystal L. Lewis, Sheila F. Castañeda, Rudolph P. Rull, for the Millennium Cohort Study Team

“…Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. …”
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Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls by Filippo Moggioli, Óscar Rodríguez-López, Elena Bocos-Corredor, Constantino Antonio García, Sonia Liébana, Tomás Pérez-Fernández, Cristina Sánchez, Susan Armijo-Olivo, José Angel Santos-Paz, Aitor Martín-Pintado-Zugasti

“…Movement was characterized using fifteen features, incorporated into machine learning models to assess how movement patterns relate to patient condition. …”
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Dynamic monitoring of fine-grained ecological vulnerability in dryland urban agglomeration integrating novel remote sensing index and explainable machine learning by Chunqiang Li, Shanchuan Guo, Qin Huang, Haowei Mu, Bo Yuan, Zilong Xia, Hong Fang, Wei Zhang, Pengfei Tang, Peijun Du

“…However, persistent technological gaps in large-scale, fine-grained and long-term monitoring hinder a comprehensive understanding of vulnerability patterns in these fragile regions. To address this, a novel Dryland Ecological Vulnerability Index (DEVI) is proposed by integrating six key indicators and combining remote sensing and machine learning to simplify the complex vulnerability scoping diagram (VSD). …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…By collecting and analyzing routine blood data, machine learning models were built to identify the patterns of changes in blood indicators related to HF.MethodsWe conducted a statistical analysis of routine blood data from 226 patients who visited Zhejiang Provincial Hospital of Traditional Chinese Medicine (Hubin) between May 1, 2024, and June 30, 2024. …”
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Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method by Junjie Chen, Hao Zeng, Zhanglei Pan, Miao Li, Qingfeng Zhou, Kaichen Chen, Yulan Hao, Xiangke Cao, Lei Zhang, Qian Wang

“…BKMR and ML further demonstrated both cumulative effects and interaction patterns within the metal mixture that collectively influenced blood pressure. …”
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Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation by Yuan Meng, Rong Hu, Song-Bin Guo, Deng-Yao Liu, Zhen-Zhong Zhou, Hai-Long Li, Wei-Juan Huang, Xiao-Peng Tian

“…However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. …”
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Effects of sandblasting and acid etching on the surface properties of additively manufactured and machined titanium and their consequences for osteoblast adhesion under different s... by Osman Akbas, Amit Gaikwad, Amit Gaikwad, Leif Reck, Nina Ehlert, Nina Ehlert, Anne Jahn, Jörg Hermsdorf, Andreas Winkel, Andreas Winkel, Meike Stiesch, Meike Stiesch, Andreas Greuling

“…For this purpose, the parameters cell adhesion, morphology, and membrane integrity were investigated using confocal laser microscopy and LDH assay.ResultsInitial high roughness of AM titanium surfaces was decreased by sandblasting, while initial smooth machined surfaces (MM) increased in roughness. Acid etching introduced characteristic irregular patterns on the surface with only marginal consequences for the resulting overall roughness. …”
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Dual pathways of forest landscape dynamics in China: Integrating ecological land evolution Index (ELEI) and machine learning to decipher fragmentation and restoration by Yifan Xie, Sui Liu, Bowen Pang, Nannan Wang, Jiwei Xu, He Huang, Yao Cui, Jiaming Yang, Yaolin Liu, Yanfang Liu

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Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches by Junfeng Ge, Lin Zhu, Sijie Jiang, Wenyan Li, Rongzhan Lin, Jun Wu, Fengying Dong, Jin Deng, Yi Lu

“…Conclusion DII, a marker of pro-inflammatory dietary patterns, was significantly linked to CKM syndrome progression and mortality, partly by influencing biological aging. …”
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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…In a blinded manner, videos were shown to 3 experts who were asked to interpret them based on their mucosal and microvasculature patterns and presence of nodularity and ulceration as well as overall suspected diagnosis. …”
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WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

“…However, the magnitude and spatial patterns of high-latitude CH<span class="inline-formula">₄</span> emissions remain relatively uncertain. …”
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Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging by Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová

“…A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…To confirm clinical utility, a decision curve analysis and temporal pattern for model prediction during the ICU stay were performed. …”
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