Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma by Pancheng Wu, Yi Zheng, Wei Wu, Beichen Zhang, Yichang Wang, Mingjing Zhou, Ziyi Liu, Zhao Wang, Maode Wang, Jia Wang

“…In addition, the tumor microenvironment between high and low MLDPS groups displayed different patterns while more tumor-infiltrating immune cells were observed in high MLDPS group. …”
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Fine-grained analysis and mapping of urban flood susceptibility with interpretable machine learning: A case study of Hefei, China by Ziyao Xing, Guijia Lyu, Yu Yao, Zhe Liu, Xiaodong Zhang

“…This paper proposes a novel approach combining interpretable machine learning and spatial autocorrelation. An ensemble learning model assesses susceptibility by incorporating terrain, urban construction, and precipitation factors. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…CNN-AHM combines spatial feature extraction with attention-based prioritization of relevant signal patterns, enhancing both accuracy and interpretability. …”
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Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience by Md Touhidul Islam, Sujan Chandra Roy, Nusrat Jahan, Al-Mahmud, Md Mazharul Islam, Abdullah Al Ferdaus, Kazunori Fujisawa, A.K.M. Adham

“…This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). …”
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Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir by Dung Bui, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Adewale Amosu, William Ampomah

“…Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. …”
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Optimization of heat and mass transfer in chemically radiative nanofluids using Cattaneo-Christov fluxes and advanced machine learning techniques by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah S. Berrouk, Waseem Khan, Saeed Islam

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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model by Sicong He, Yanbin Yuan, Zhen Li, Heng Dong, Xiaopang Zhang, Zili Zhang, Lan Luo

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Game Theoretic Approach to QoS Oriented Machine Learning Model Development Toward 5G Network Migration Planning by Arjun Ray, Manish Kr. Yadav, Babu R. Dawadi, Krishna R. Bhandari

“…The second phase employs evolutionary game theory to observe the migration patterns for both core and RAN components of interconnected telecom operators in three distinct scenarios over a span of five years. …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…However, in general, the performance of machine learning in recognising patterns is proportional to the size of the dataset. …”
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Vapor pressure deficit (VPD) downscaling based on multi-source remote sensing, in-situ observation, and machine learning in China by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…New hydrological insights for the region: Machine learning-based downscaling methods offer a potential solution to enhance the accuracy of VPD spatiotemporal distribution. …”
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Integration of machine learning and bulk sequencing revealed exosome-related gene FOSB was involved in the progression of abdominal aortic aneurysm by Xianlu Ma, Xianlu Ma, Hongjie Zhou, Hongjie Zhou, Ren Wang, Ren Wang

“…The expression of contraction-related markers α-SMA and SM22α, and the synthetic marker OPN, was analyzed by qRT-PCR and Western blot.ResultsA total of 44 differentially expressed genes were identified, revealing distinct expression patterns between AAA and normal samples. WGCNA identified two key gene modules that were strongly correlated with immune and inflammatory responses, with the hub genes from these modules enriched in immune-related pathways. …”
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A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study by Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

“…Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. …”
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Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery by Arezoo Abasi, Ahmad Nazari, Azar Moezy, Seyed Ali Fatemi Aghda

“…However, traditional statistical models often fail to leverage the full potential of CPET data in predicting reinjury. Machine learning (ML) algorithms offer promising capabilities in uncovering complex patterns within this data, allowing for more accurate injury risk assessment. …”
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Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts by ChuXia Tan, Yuan Liu, Lijun Li, Ying Li, Pingting Yang, Yinglong Duan, Xingxing Wang, Huiyi Zhang, Jingying Wang, Honglian Zhang

“…Abstract Background Hyperuricemia (HUA) as a global public health challenge, although its overall epidemiological characteristics have been widely reported, its age-specific risk pattern remains controversial. This study aims to reveal the risk factors of HUA in healthy physical examination populations of different age groups and construct a machine learning-driven risk prediction model to achieve precise intervention. …”
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Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies by Nuray Vakitbilir, Abrar Islam, Alwyn Gomez, Kevin Y. Stein, Logan Froese, Tobias Bergmann, Amanjyot Singh Sainbhi, Davis McClarty, Rahul Raj, Frederick A. Zeiler

“…Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. …”
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Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods by Zuopeng Zhang, Zhe Li, Zhirong Li

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Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer by Longpeng Li, Longpeng Li, Jinfeng Zhao, Yaxin Wang, Zhibin Zhang, Wanquan Chen, Jirui Wang, Yue Cai

“…The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.MethodsThe levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. …”
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SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses by Kaiping Luo, Kaiping Luo, Donghui Xing, Donghui Xing, Xiang He, Yixin Zhai, Yanan Jiang, Hongjie Zhan, Zhigang Zhao

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.ResultsTwo molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”
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Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines by Qin Li, Nan Lin, Zuheng Wang, Yuexi Chen, Yuli Xie, Xuemei Wang, Jirui Tang, Yuling Xu, Min Xu, Na Lu, Yiqian Huang, Jiamin Luo, Zhenfang Liu, Li Jing

“…This study aimed to analyze pathogen distribution, drug-resistance patterns and develop a novel predictive model for 30-day mortality in HM patients with BSIs. …”
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Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients by Thomas Meredith, Thomas Meredith, Thomas Meredith, Farhan Mohammed, Amy Pomeroy, Sebastiano Barbieri, Sebastiano Barbieri, Erik Meijering, Louisa Jorm, David Roy, Jason Kovacic, Jason Kovacic, Jason Kovacic, Jason Kovacic, Michael Feneley, Michael Feneley, Michael Feneley, Christopher Hayward, Christopher Hayward, Christopher Hayward, David Muller, David Muller, David Muller, Mayooran Namasivayam, Mayooran Namasivayam, Mayooran Namasivayam

“…BackgroundLong-term mortality risk is seldom re-assessed in contemporary clinical practice following successful transcatheter aortic valve implantation (TAVR). Unsupervised machine learning permits pattern discovery within complex multidimensional patient data and may facilitate recognition of groups requiring closer post-TAVR surveillance.MethodsWe analysed and differentiated routinely collected demographic, biochemical, and cardiac imaging data into distinct clusters using unsupervised machine learning. k-means clustering was performed on data from 200 patients who underwent TAVR for severe aortic stenosis (AS). …”
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