Integrated transcriptomics and machine learning reveal REN as a dual regulator of tumor stemness and NK cell evasion in Wilms tumor progression by Qingfei Cao, Junyi Li, Yunfei Zou, Changwen Xu, Huihui Tang, Meixue Chen

“…A novel Cancer Stemness Prognostic Index (CSPI) was developed using machine learning algorithms to stratify WT patients by risk and histological subtype. …”
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Development and external validation of a machine learning model for predicting drug-induced immune thrombocytopenia in a real-world hospital cohort by Hoang Van Dung, Vu Manh Tan, Nguyen Thi Dieu, Pham Van Linh, Nguyen Van Khai, Tran Thi Ngan, Nguyen Thi Thu Phuong

“…SHAP analysis identified AST, baseline platelet count, and renal function as key contributors. DCA and clinical impact curves demonstrated potential benefit in supporting real-time risk stratification. …”
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Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change by Hua Cheng, Kasper Johansen, Baocheng Jin, Shiqin Xu, Xuechun Zhao, Liqin Han, Matthew F. McCabe

“…Biological invasions pose significant threats to ecosystem structure and function, disrupt ecosystem services, cause high economic losses, and negatively impact human well-being. …”
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ABBA+BraiAn, an integrated suite for whole-brain mapping, reveals brain-wide differences in immediate-early genes induction upon learning by Nicolas Chiaruttini, Carlo Castoldi, Linda Maria Requie, Carmen Camarena-Delgado, Beatrice Dal Bianco, Johannes Gräff, Arne Seitz, Bianca A. Silva

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Identification of multiomics and immune infiltration-associated biomarkers for early gastric cancer: a machine learning-based diagnostic model development study by Kewei Du, Wenfei Hu, Shan Gao, Jianxin Gan, Chongge You, Shangdi Zhang

“…Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. …”
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Diagnostic potential of the B9D2 gene in colorectal cancer based on whole blood gene expression data and machine learning by Zhaorui Wang, Yongcheng Fu, Haozhe Zhang, Na Liu, Ningjing Lei

“…The B9D2 gene, which is essential for ciliary function, has been rarely explored in CRC. This study is the first to investigate the diagnostic potential of B9D2 in CRC, using bioinformatics and machine learning to uncover its novel role in early detection, with implications for clinical translation. …”
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Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study by Heonyi Lee, Yi-Jun Kim, Jin-Hong Kim, Soo-Kyung Kim, Tae-Dong Jeong

“…Subgroup analyses demonstrated consistent performance across different patient categories, such as renal function, sex, and BMI. A web-based TDM analysis tool was developed using the OPTIVAN algorithm. …”
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Cognitive-behavioral therapy to normalize social learning for patients with major depressive disorders: study protocol for a single-arm clinical trial by Yuening Jin, Si Zu, Pengchong Wang, Fangrui Sheng, Xue Wang, Yun Wang, Qun Chen, Jie Zhong, Fang Yan, Jia Zhou, Zhanjiang Li, Yuan Zhou

“…Abstract Background The current study aims to explore the efficacy of cognitive behavioral therapy (CBT) in normalizing social learning capabilities and its underlying neural processes among patients with MDD, in terms of enhancing learning towards positive social feedback, and reducing excessive learning towards negative social feedback. …”
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A Prediction Model of Stable Warfarin Doses in Patients After Mechanical Heart Valve Replacement Based on a Machine Learning Algorithm by Bowen Guo, Cong Chen, Junhang Jia, Jubing Zheng, Yue Song, Taoshuai Liu, Kui Zhang, Yang Li, Ran Dong

“…The support vector machine radial basis function (SVM Radial) algorithm showed the best performance of all models, with the highest R2 value of 0.98 and the lowest MAE of 0.14 mg/day (95% confidence interval (CI): 0.11–0.17). …”
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Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach by Mengru Liu, Mengling Feng, Kewin Tien Ho Siah, Wesley Yeung, Hao Du, Valencia Zhang Ru-Yan, Readon Teh, Christopher Yu En Tan, Christina Scaduto, Sarah Bolongaita, Maria Teresa Kasunuran Cruz, Xiaohao Lin, Yan Yuan Tan

“…We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. …”
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Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study by Xue Dong, Xibin Jia, Wei Zhang, Jingxuan Zhang, Hui Xu, Lixue Xu, Caili Ma, Hongjie Hu, Jiawen Luo, Jingfeng Zhang, Zhenchang Wang, Wenbin Ji, Dawei Yang, Zhenghan Yang

“…An adversarial network-based deep learning (AD-DL) model was developed to learn domain-invariant features from multiple centers within the training set. …”
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Klasifikasi Aktivitas Manusia Menggunakan Algoritme Computed Input Weight Extreme Learning Machine dengan Reduksi Dimensi Principal Component Analysis by M. Sofyan Irwanto, Fitra A. Bachtiar, Novanto Yudistira

“…The best hyperparameter obtained for the PCA algorithm is with a value of k = 207, and for the CIW-ELM algorithm with the number of hidden neurons = 600 and the sigmoid activation function. The accuracy results obtained in this study were 0,957 and the f-measure average were 0,958 with a training time of 0,57 seconds. …”
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Optimizing Attenuation Correction in ⁶⁸Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement by Masoumeh Dorri Giv, Guluzar Ozbolat, Hossein Arabi, Somayeh Malmir, Shahrokh Naseri, Vahid Roshan Ravan, Hossein Akbari-Lalimi, Raheleh Tabari Juybari, Ghasem Ali Divband, Nasrin Raeisi, Vahid Reza Dabbagh Kakhki, Emran Askari, Sara Harsini

“…These outliers were excluded, and the refined dataset was used to retrain the model with an L2 loss function. Performance was evaluated using metrics including mean error (ME), mean absolute error (MAE), relative error (RE%), RMSE, and SSIM on both internal and external test datasets. …”
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Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease feat... by Vivien Batoumeni, Yeranuhi Hovhannisyan, Bénédicte Gobert, Keyhan Alvandipour, Jennifer Arthur Ataam, Ombline Conrad, David Hoffmann, Nicolas Wiest-Daesslé, Jochen Dobner, José Américo Nabuco Leva Ferreira Freitas, Hakim Hocini, Zhenlin Li, Laurent Brino, Andrea Rossi, Onnik Agbulut, Peter Sommer, Pierre Joanne, Konstantinos Gkatzis

“…Using advanced technologies, including transcriptomics and phenotypic machine learning algorithms, we analyzed how this mutation disrupts cellular function and contributes to disease phenotypes. …”
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Blood metabolome shows signatures of metabolic dysregulation in obese and overweight subjects that can be predicted by machine learning applied to heart rate variability by Andrea Di Credico, Andrea Di Credico, Andrea Di Credico, David Perpetuini, Pascal Izzicupo, Giulia Gaggi, Giulia Gaggi, Giulia Gaggi, Claudia Rossi, Claudia Rossi, Arcangelo Merla, Barbara Ghinassi, Barbara Ghinassi, Barbara Ghinassi, Angela Di Baldassarre, Angela Di Baldassarre, Angela Di Baldassarre, Ines Bucci, Ines Bucci

“…Metabolic health is also closely linked to autonomic function, as measured by heart rate variability (HRV), making HRV a potential non-invasive indicator of metabolic status. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. …”
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Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study by Andrea Campagner, Luisa Agnello, Anna Carobene, Andrea Padoan, Fabio Del Ben, Massimo Locatelli, Mario Plebani, Agostino Ognibene, Maria Lorubbio, Elena De Vecchi, Andrea Cortegiani, Elisa Piva, Donatella Poz, Francesco Curcio, Federico Cabitza, Marcello Ciaccio

“…However, making ML models function in clinical practice may be problematic, as their performance may suffer when deployed in contexts other than the research environment. …”
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Association of rs440446 in the APOE intron region with multidimensional cognitive function, lipid/metabolic markers, and spontaneous neural activity in Chinese urban community-dwel... by Zhiyuan Wang, Hongyu Guo, Jianshuai Li, Xuejie Hu, Xin Li, Jinping Sun

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Effect of home-based transcranial direct current stimulation (tDCS) on cognitive functioning in bipolar depression: an open-label, single-arm acceptability and feasibility study by Hakimeh Rezaei, Rachel D. Woodham, Ali-Reza Ghazi-Noori, Philipp Ritter, Elvira Bramon, Michael Bauer, Allan H. Young, Cynthia H. Y. Fu

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Control Method in Coordinated Balance with the Human Body for Lower-Limb Exoskeleton Rehabilitation Robots by Li Qin, Zhanyi Xing, Jianghao Wang, Guangtong Lu, Houzhao Ji

“…We propose a balance-directed motion generator (BDMG) based on the principles of deep reinforcement learning. The reward function sub-components pertaining to physiological guidance and compliant assistance were designed to explore motion instructions that are harmoniously aligned with the human body’s balance correction mechanisms. …”
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