A Convolutional Mixer-Based Deep Learning Network for Alzheimer’s Disease Classification from Structural Magnetic Resonance Imaging by M. Krithika Alias Anbu Devi, K. Suganthi

“…<b>Objective:</b> Alzheimer’s disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to late sixties. …”
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Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson’s Disease Mobility Assessments by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Sunita Shakya, Jeffrey M. Hausdorff, Rainer von Coelln, Michael P. Cummings

“…Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson’s disease (PD) on motor control, balance, and cognitive function. We assess the test–retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. …”
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Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning by Junlong Ma, Heng Chen, Ji Sun, Juanjuan Huang, Gefei He, Guoping Yang

“…The trained models, in conjunction with liver function laboratory tests, were used to thoroughly and efficiently identify liver injury cases. …”
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Revealing Potential Diagnostic Gene Biomarkers Associated with Immune Infiltration in Patients with Renal Fibrosis Based on Machine Learning Analysis by Yu-Chao Sun, Zhen-Zhen Qiu, Fu-Li Wen, Jin-Quan Yin, Hao Zhou

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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors by Yuanyuan Zhao, Jinzhu Jia

“…Methods This study proposes an extension of the NOTEARS algorithm, termed DAGSLAM, which is designed for Bayesian network structure learning with mixed-type data. The algorithm integrates continuous and categorical variables through a tailored loss function, enhancing its applicability to real-world epidemiological datasets. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…Abstract Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. …”
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Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study by Aimin Li, Yueying Liu, Yufan Luo, Xue Xiao, Wei Xiao, Ruijin Xie, Xianhui Deng, Zhe Chen, Qian Zhou, Yue Gong, Zhen Chen, Hua Xu

“…Clinical data, encompassing complete blood counts, liver and kidney function assessments, blood glucose levels, and serum electrolyte analyses, were collected. …”
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Prediction of Voice Therapy Outcomes Using Machine Learning Approaches and SHAP Analysis: A K-VRQOL-Based Analysis by Ji Hye Park, Ah Ra Jung, Ji-Na Lee, Ji-Yeoun Lee

“…Integrating statistical and machine learning approaches provides a robust framework for predicting and interpreting voice therapy outcomes. …”
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Prefrontal serotonin depletion delays reversal learning and increases theta synchronization of the infralimbic-prelimbic-orbitofrontal prefrontal cortex circuit by Yoana Estrada-Reyes, Yoana Estrada-Reyes, José Miguel Cervantes-Alfaro, Miguel Ángel López-Vázquez, María Esther Olvera-Cortés

“…However, the mechanisms by which serotonin modulates the prefrontal cortex functions during reversal learning remain unclear. …”
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Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis by Haihua Ying, Andri Pranolo, Zalik Nuryana, Andini Isti Syafitri

“…Neuropsychological evaluations are valuable in neurosurgery because they comprehensively evaluate cognitive, affective, and behavioral functioning to optimize patient outcomes. Incorporating artificial intelligence (AI) into neuropsychology offers optimistic advances, with machine learning models assisting in classifying behavioral, cognitive, and functional impairments while minimizing the number of tests. …”
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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry by Valeria Di Stefano, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, Luca Steardo

“…Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. …”
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Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer by Zijing Wang, Zhanyuan Sun, Hengyi Lv, Wenjun Wu, Hai Li, Tao Jiang

“…The immunological responses to cancer heavily rely on the function of CD4Tconv. Despite this critical role, prognostic studies on CRC-related CD4Tconv remain insufficient. …”
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Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features by G. Elangovan, N. Jagadish Kumar, J. Shobana, M. Ramprasath, Gyanendra Prasad Joshi, Woong Cho

“…There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. …”
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Improved A-Line and B-Line Detection in Lung Ultrasound Using Deep Learning with Boundary-Aware Dice Loss by Soolmaz Abbasi, Assefa Seyoum Wahd, Shrimanti Ghosh, Maha Ezzelarab, Mahesh Panicker, Yale Tung Chen, Jacob L. Jaremko, Abhilash Hareendranathan

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Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation by Zixuan Wu, Jinfeng Xu, Yuan Gao, Kang Tan, Xiaolei Yao, Qinghua Peng

“…Enrichment analysis revealed that gene sets positively correlated with LTF were enriched in immune-related pathways. For biological function analysis in LTF, retina homeostasis, sensory perception of bitter taste, and tissue homeostasis were emphasized. …”
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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies by Yingchun He, Yi-haw Jan, Fan Yang, Yunru Ma, Chun Pei

“…Leveraging the KR algorithm and signal processing techniques, the proposed method is designed for individualized motor function evaluation in home or community-based settings.…”
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Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

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Classification of collagen remodeling in asthma using second-harmonic generation imaging, supervised machine learning and texture-based analysis by Natasha N. Kunchur, Joshua J. A. Poole, Jesse Levine, Tillie-Louise Hackett, Rebecca Thornhill, Rebecca Thornhill, Leila B. Mostaço-Guidolin

“…Airway remodeling is present in all stages of asthma severity and has been linked to reduced lung function, airway hyperresponsiveness and increased deposition of fibrillar collagens. …”
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Constraint-incorporated deep learning model for predicting heat transfer in porous media under diverse external heat fluxes by Ziling Guo, Hui Wang, Huangyi Zhu, Zhiguo Qu

“…This study presents a deep learning model with physical constraints for predicting heat conduction in porous media, alleviating the burden of extensive experiments and simulations.…”
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Reinforcement Learning Combined With a Self-Retraining Strategy for Dual-Comb State Generation in a Single-Cavity Laser by Zhiwei Fang, Guoqing Pu, Chao Luo, Yunhao Xie, Weisheng Hu, Lilin Yi

“…Here, reinforcement learning combined with a self-retraining strategy is employed in a dual-wavelength SCDCL to achieve stable recovery in varying environments. …”
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