Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression by Jinli Gao, Qinglang Wang, Jie Liu, Siqian Zheng, Jiahong Liu, Zhiyong Gao, Cheng Zhu

“…Notably, Oprm1 exhibited the highest feature importance, contributing to a model accuracy of 94.5%. Gene expression patterns showed strong consistency across the prefrontal cortex (PFC) and nucleus accumbens (NAC).ConclusionThe combined application of machine learning and transcriptomic analysis effectively identified core neurobiological genes in a depression model. …”
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Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente, Kleber Trabaquini

“…For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. …”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. …”
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Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) by Daniel Lafontaine, Stephanie Chahwan, Gustavo Barraza, Burcin Agridag Ucpinar, Gunjan Kayal, Nicolás Gómez-Banoy, Paul Cohen, John L. Humm, Heiko Schöder

“…In the context of cancer care, artificial intelligence (AI)-driven BAT detection holds immense promise for rapid and automatic differentiation between malignant lesions and non-malignant BAT confounds. By leveraging machine learning to discern intricate patterns in imaging data, this study aims to advance the automation of BAT recognition and provide precise quantitative assessment of radiographic features. …”
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Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos by Yi-Hung Chiu, Ying-Han Lee, San-Yuan Wang, Chen-Sen Ouyang, Rong-Ching Wu, Rei-Cheng Yang, Lung-Chang Lin

“…A classification analysis based on six machine learning models was performed to compare the performance indices and the discriminatory power of various features. …”
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Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso by Oussama Himmy, Thanh Thi Nguyen, Katharina Salomea Hemmler, Safia Loulad, Hassan Rhinane, Andreas Buerkert

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A human-machine collaborative approach for high-resolution monitoring of suspended sediment dynamics in data-scarce and optically complex waters by Hai Sun, Yanan Chu, Bingchen Liang, Huiqian Wang, Chao Fan

“…We propose a transfer-based human–machine collaborative learning approach that embeds expert knowledge to significantly reduce reliance on field data. …”
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Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning by Qi Mengjuan, Guo Luo, Liu Wenshu, Wang Weiyin, Jiang Chunqian, Bai Yanfeng

“…Analyzing the spatiotemporal patterns of forest ecosystem services (FESs) and their climatic drivers in the hilly mountainous regions of southern China (CSHR) is crucial for advancing regional ecological conservation. …”
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Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…This study proposes a framework that integrates machine learning with explainable artificial intelligence (XAI) to predict and analyze agricultural droughts in the Ta-pieh Mountains. …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…In contrast, classical machine learning techniques offer a more efficient alternative but are often overlooked due to a lack of focus on data pre-processing, which is critical for achieving optimal performance. …”
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Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature by Assmaa Abd-Elmonem, Zill E Shams, Mariam Imtiaz, Kashif Ali, Sohail Ahmad, Wasim Jamshed, Fayza Abdel Aziz ElSeabee, Neissrien Alhubieshi, Syed M. Hussain, Hijaz Ahmad

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Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention by Anna Rita E. Moukarzel, Justin Fitzgerald, Marcus Battraw, Clifford Pereira, Andrew Li, Paul Marasco, Wilsaan M. Joiner, Jonathon Schofield

“…We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. …”
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Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach by Carlo Giaquinto, Daniela Paolotti, Daniele Donà, Stefania Fiandrino, Piero Poletti, Michael Davis Tira, Costanza Di Chiara

“…It aims to identify patterns in COVID-19 morbidity by clustering individuals based on symptom similarities and duration of symptoms and develop a machine-learning tool to classify new cases into risk groups.Methods We propose a data-driven approach to explore changes in COVID-19 characteristics by analysing data from 581 children and adolescents collected within a paediatric cohort at the University Hospital of Padua. …”
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Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces by Jun Li, Jia-Yi Lu, Xin-Ying Tuo, Chao Wang, Jun-Zhuo Liu, Zhan-Dong Gao, Cun-Hao Yu, Fei Zang

“…Elevated concentrations, particularly of Zn, Cd, Pb, and Hg, displayed distinct spatial patterns linked to industrial activities and urban development. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…The cellular expression patterns of signature genes were further validated using our own scRNA-seq dataset. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Restricted cubic spline regression examined dose–response patterns, and subgroup analyses evaluated effect modifiers. …”
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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach by Yu CS, Wu JL, Shih CM, Chiu KL, Chen YD, Chang TH

“…To improve the explainability of the AI models, Shapley Additive Explanations methods were also conducted.Conclusion: Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients’ health quality in the hospital.Keywords: mortality, risk factor, cardiovascular disease, multivariate statistical analysis, machine learning, artificial intelligence…”
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Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study by Amanda de Carvalho Dutra, Lincoln Luis Silva, Isadora Martins Borba, Amanda Gubert Alves dos Santos, Diogo Pinetti Marquezoni, Matheus Henrique Arruda Beltrame, Rogério do Lago Franco, Ualid Saleh Hatoum, Juliana Harumi Miyoshi, Gustavo Cezar Wagner Leandro, Marcos Rogério Bitencourt, Oscar Kenji Nihei, João Ricardo Nickenig Vissoci, Luciano de Andrade

“…Objective: To analyze the predictability of IHD mortality using machine learning (ML) techniques in combination with geospatial analysis in southern Brazil. …”
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Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Background: Controversy remains about whether to pin the contralateral side in cases of unilateral slipped capital femoral epiphysis (SCFE). Machine learning (ML) algorithms can be leveraged to identify complex, nonlinear patterns in data and allow for more accurate predictions on which patients may need a prophylactic pin. …”
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Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment by Julie Vinkel, Alfonso Buil, Ole Hyldegaard

“…We aimed to identify gene expression patterns associated with effects of HBO2 treatment in patients with sepsis caused by NSTI, and to explore sepsis-NSTI profiles that are more receptive to HBO2 treatment. …”
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