A differential diagnostic model based on immunological evaluation and routine laboratory tests: distinguishing multiple myeloma from other disorders with aberrant immunoglobulin el... by Manning Li, Sixun Jia, Han Wu, Chunyi Shou, Xiaolu Song, Ye Peng, Huafang Wang, Ying Wang, Xiangmin Tong, Yirui Chen

“…A discriminative diagnostic model was developed using a multivariate logistic regression algorithm. …”
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Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis by Siyu Wei, Zehong Yue, Chen Sun, Yuping Zou, Haiyan Chen, Junxian Tao, Jing Xu, Yuan Xu, Ning Wang, Yan Guo, Qinduo Ren, Chang Wang, Songlin Lu, Ye Ma, Yu Dong, Chen Zhang, Hongmei Sun, Guoping Tang, Fanwu Kong, Wenhua Lv, Zhenwei Shang, Mingming Zhang, Yongshuai Jiang, Hongchao Lyu

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Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3 by Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang, Kai Zhang, Feiyue Zhang, Yiluo Xie, Hongyu Ma, Linfeng Pan, Qiang Zhang, Min Lu, Hongtao Wang, Chaoqun Lian

“…Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…A demo of the DMS-PSO-SVM modeling algorithm code used in this study can be found on Github (https://github.com/Edward-E-S-Wang/DMS-PSO-SVM).…”
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The role of hypoxia-senescence co-related molecular subtypes and prognostic characteristics in hepatocellular carcinoma by Fuqing Chen, Yifan Cai, Xiangmei Chen, Changzhou Chen, Qinliang Fang, Jianming Liu, Yibin Zhang, Jianyin Zhou

“…SVM algorithm was used to classify HCC patients based on HSCRGs. …”
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Non-Invasive Detection of Breast Cancer by Low-Coverage Whole-Genome Sequencing from Plasma by Li Peng, Ru Yao, Sihang Gao, Yang Qu, Li Qu, Jingbo Zhang, Yidong Zhou

“…Our approach adopted principal component analysis and a generalized linear model algorithm to distinguish between breast cancer and normal samples. …”
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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

“…Patient-specific efficacy and prognosis prediction models were then constructed using various machine learning algorithms.ResultsTotal bile acids (TBAs) and CA-199 present as an independent risk factor in Cox multivariate analysis for primary and recurrent ovarian cancer patients respectively (P < 0.05). …”
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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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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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