Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke by Yuqi Tang, Sixian Hu, Yipeng Xu, Linjia Wang, Yu Fang, Pei Yu, Yaning Liu, Jiangwei Shi, Junwen Guan, Ling Zhao

“…We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. …”
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Development of a PANoptosis-related LncRNAs for prognosis predicting and immune infiltration characterization of gastric Cancer by Yangjian Hong, Cong Luo, Yanyang Liu, Zeng Wang, Huize Shen, Wenyuan Niu, Jiaming Ge, Jie Xuan, Gaofeng Hu, Bowen Li, Qinglin Li, Huangjie Zhang

“…PANoptosis-related genes were obtained from molecular characteristic databases, and PANlncRNAs were screened through Pearson correlation analysis. Based on this, PANlncRNAs were subjected to univariate Cox regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm to obtain lncRNA associated with survival outcomes, which were subsequently used to calculate survival scores and to construct signatures. …”
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Regional Brain Aging Disparity Index: Region-Specific Brain Aging State Index for Neurodegenerative Diseases and Chronic Disease Specificity by Yutong Wu, Shen Sun, Chen Zhang, Xiangge Ma, Xinyu Zhu, Yanxue Li, Lan Lin, Zhenrong Fu

“…This study proposes a novel brain-region-level aging assessment paradigm based on Shapley value interpretation, aiming to overcome the interpretability limitations of traditional brain age prediction models. Although deep-learning-based brain age prediction models using neuroimaging data have become crucial tools for evaluating abnormal brain aging, their unidimensional brain age–chronological age discrepancy metric fails to characterize the regional heterogeneity of brain aging. …”
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Predicting immune status and gene mutations in stomach adenocarcinoma patients based on inflammatory response-related prognostic features by Huanjun Li, Jingtang Chen, Zhiliang Chen, Jingsheng Liao

“…Genes associated with STAD prognosis were obtained from the intersection of inflammation-related genes and DEGs. The key genes screened by last absolute shrinkage and selection operator (LASSO) Cox and stepwise regression analyses were used to construct prognostic models and nomograms. …”
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Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model by Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang

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Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospe... by Meng Zheng, Xiaowei Zhang, Haihong Wang, Ping Yuan, Qiulan Yu

“…Based on the variables screened out by ridge regression and Boruta algorithm, univariate and multivariate logistic regression analyses were further adopted. …”
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Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration by Kaili Liao, Minqi Zhu, Lei Guo, Zijun Gao, Jinting Cheng, Bing Sun, Yihui Qian, Bingying Lin, Jingyan Zhang, Tingyi Qian, Yixin Jiang, Yanmei Xu, Qionghui Zhong, Xiaozhong Wang

“…Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. …”
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Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study by Yelong Shen, Siyu Wu, Yanan Wu, Chao Cui, Haiou Li, Shuang Yang, Xuejun Liu, Xingzhi Chen, Chencui Huang, Ximing Wang

“…The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. …”
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Corrosion Rate Prediction of Buried Oil and Gas Pipelines: A New Deep Learning Method Based on RF and IBWO-Optimized BiLSTM–GRU Combined Model by Jiong Wang, Zhi Kong, Jinrong Shan, Chuanjia Du, Chengjun Wang

“…The combined model, which incorporates an intelligent algorithm, is an effective means of enhancing the precision of buried pipeline corrosion rate prediction. …”
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Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer by Lin Ni, Lin Ni, He Li, Yanqi Cui, Wanqiu Xiong, Shuming Chen, Hancong Huang, Zhiwei Wang, Hu Zhao, Hu Zhao, Hu Zhao, Bing Wang, Bing Wang, Bing Wang

“…Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…More importantly, our proposed model identified all the AF patients that were diagnosed with Holter monitoring during index stroke admission. …”
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Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study by Ying Wu, Rui Xv, Qinyun Chen, Ranran Zhang, Min Li, Chen Shao, Guoxi Jin, Guoxi Jin, Xiaolei Hu, Xiaolei Hu

“…LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. …”
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Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases by Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He

“…By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.…”
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Prospective external validation of the automated PIPRA multivariable prediction model for postoperative delirium on real-world data from a consecutive cohort of non-cardiac surgery... by Mary-Anne Kedda, Kelly A Reeve, Nayeli Schmutz Gelsomino, Michela Venturini, Felix Buddeberg, Martin Zozman, Reto Stocker, Philipp Meier, Marius Möller, Simone Pascale Wildhaber, Benjamin T Dodsworth

“…The study highlighted the model’s applicability across diverse clinical environments, despite differences in patient populations and screening protocols.Conclusions The PIPRA algorithm is a reliable tool for identifying surgical patients at risk of POD, supporting early intervention strategies to improve patient outcomes. …”
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Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques by Juswin Sajan John, Boppuru Rudra Prathap, Gyanesh Gupta, Jaivanth Melanaturu

“…Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data.The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). …”
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Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] by Emily Kendall, Lucia Cilloni, Nimalan Arinaminpathy, David Dowdy

“…Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing. …”
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Using Life’s Essential 8 and heavy metal exposure to determine infertility risk in American women: a machine learning prediction model based on the SHAP method by Xiaoqing Gu, Qianbing Li, Xiangfei Wang

“…The association between LE8 and heavy metal exposure and risk of infertility was assessed using logistic regression analysis and six machine learning models (Decision Tree, GBDT, AdaBoost, LGBM, Logistic Regression, Random Forest), and the SHAP algorithm was used to explain the model’s decision process.ResultsOf the six machine learning models, the LGBM model has the best predictive performance, with an AUROC of 0.964 on the test set. …”
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Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception–ResNet Model by Md. Ahasan Kabir, Ivan Lee, Sang-Heon Lee

“…A feature selection algorithm was employed to enhance processing efficiency and reduce spectral dimensionality while maintaining high classification accuracy. …”
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Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer by Fengqiang Cui, Changjiao Yan, Jiang Wu, Yuqing Yang, Jixin Yang, Jialing Luo, Nanlin Li

“…Next, 101 combinations of 10 machine learning algorithms and univariate Cox analysis were utilized to screen for prognostic genes. …”
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AI-driven innovation in antibody-drug conjugate design by Heather A. Noriega, Xiang Simon Wang

“…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”
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