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Page: 12345 of 5

• Research PaperVolume 16, Issue 7pp 6455-6477

  Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer

  Relevance score: 0.00

  Guo-Xing Li, Yun-Peng Chen, You-Yang Hu, Wen-Jing Zhao, Yun-Yan Lu, Fu-Jian Wan, Zhi-Jun Wu, Xiang-Qian Wang, Qi-Ying Yu

  Keywords: gastric cancer, cancer stemness, machine learning, ssGSEA

  Published in Aging on April 12, 2024

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• Research PaperVolume 16, Issue 7pp 6314-6333

  Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke

  Relevance score: 0.00

  Jinzhi Liu, Zhihua Si, Ju Liu, Xu Zhang, Cong Xie, Wei Zhao, Aihua Wang, Zhangyong Xia

  Keywords: ischemic stroke, coagulation genes, immune infiltration analysis, machine learning, diagnosis

  Published in Aging on April 3, 2024

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• Research PaperVolume 16, Issue 5pp 4563-4578

  Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis

  Relevance score: 0.00

  Tingting Liu, Xiaomao Li, Mu Pang, Lifen Wang, Ye Li, Xizhe Sun

  Keywords: osteoarthritis, endoplasmic reticulum, diagnostic biomarker, immune microenvironment, machine learning

  Published in Aging on February 28, 2024

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• Research PaperVolume 16, Issue 4pp 3856-3879

  An assessment system for clinical and biological interpretability in ulcerative colitis

  Relevance score: 0.00

  Shiqian Zhang, Ge Zhang, Wenxiu Wang, Song-Bin Guo, Pengpeng Zhang, Fuqi Wang, Quanbo Zhou, Zhaokai Zhou, Yujia Wang, Haifeng Sun, Wenming Cui, Shuaixi Yang, Weitang Yuan

  Keywords: machine learning, diagnosis model, regulatory landscape, computational biology, biological interpretability

  Published in Aging on February 16, 2024

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• Research PaperVolume 16, Issue 3pp 2774-2788

  Machine learning-based B cell-related diagnostic biomarker signature and molecular subtypes characteristic of ulcerative colitis

  Relevance score: 0.00

  Guo-Liang Wu, Li Li, Xiao-Yao Chen, Wei-Feng Zhang, Jun-Bo Wu, Xiaoning Yu, Hong-Jin Chen

  Keywords: ulcerative colitis, B cell-related genes, diagnostic biomarker, immune infiltration, machine learning

  Published in Aging on February 5, 2024

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• Research PaperVolume 15, Issue 20pp 11162-11183

  Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer

  Relevance score: 0.00

  Xiaofang Han, Liu Yang, Hui Tian, Yuanyuan Ji

  Keywords: immunotherapy, prognostic signature, ovarian cancer, machine learning, PI3K/Akt pathway

  Published in Aging on October 17, 2023

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• Research PaperVolume 15, Issue 14pp 6848-6864

  The integration of machine learning and multi-omics analysis provides a powerful approach to screen aging-related genes and predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

  Relevance score: 0.00

  Jiahui Shen, Han Gao, Bowen Li, Yan Huang, Yinfang Shi

  Keywords: machine learning, aging, hepatocellular carcinoma, prognostic model, immunotherapy

  Published in Aging on July 28, 2023

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• Research PaperVolume 15, Issue 13pp 6152-6162

  Machine learning models predict the mTOR signal pathway-related signature in the gastric cancer involving 2063 samples of 7 centers

  Relevance score: 0.00

  Hao Zhang, Huiqin Zhuo, Jingjing Hou, Jianchun Cai

  Keywords: mTOR, machine learning, cellular senescence, ming classification

  Published in Aging on June 20, 2023

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• Research PaperVolume 15, Issue 11pp 5007-5031

  Machine learning and single cell RNA sequencing analysis identifies regeneration-related hepatocytes and highlights a Birc5-related model for identifying cell proliferative ability

  Relevance score: 0.00

  Yuan Du, Shuqin Jian, Xicheng Wang, Chao Yang, Hua Qiu, Kang Fang, Yehong Yan, Jun Shi, Jianfeng Li

  Keywords: liver regeneration, Hmgb1, Birc5, hdWGCNA, machine learning

  Published in Aging on June 14, 2023

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• Research PaperVolume 15, Issue 10pp 4465-4480

  Identification and validation of diagnostic signature genes in non-obstructive azoospermia by machine learning

  Relevance score: 0.00

  Lingxiang Ran, Zhixiang Gao, Qiu Chen, Fengmei Cui, Xiaolong Liu, Boxin Xue

  Keywords: machine learning, non-obstructive azoospermia, boruta algorithm, lasso regression, LightGBM model

  Published in Aging on May 24, 2023

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• Research PaperVolume 15, Issue 8pp 3120-3140

  An artificial neural network model to diagnose non-obstructive azoospermia based on RNA-binding protein-related genes

  Relevance score: 0.00

  Fan Peng, Bahaerguli Muhuitijiang, Jiawei Zhou, Haoyu Liang, Yu Zhang, Ranran Zhou

  Keywords: machine learning, artificial neural network, diagnosis, non-obstructive azoospermia, RNA-binding protein

  Published in Aging on April 24, 2023

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• Research PaperVolume 15, Issue 7pp 2667-2688

  Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma

  Relevance score: 0.00

  Guoyin Li, Huina Zhang, Jin Zhao, Qiongwen Liu, Jinke Jiao, Mingsheng Yang, Changjing Wu

  Keywords: melanoma, immunogenic cell death, machine learning, prognostic model, immunotherapy

  Published in Aging on April 6, 2023

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• Research PaperVolume 13, Issue 13pp 17137-17154

  Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology

  Relevance score: 0.00

  Sisi Yan, Wenyi Jin, Jinli Ding, Tailang Yin, Yi Zhang, Jing Yang

  Keywords: machine learning, prediction model, assisted reproduction technology, poor ovarian response

  Published in Aging on May 17, 2021

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• Research PaperVolume 12, Issue 10pp 9840-9854

  Development of a machine learning-based multimode diagnosis system for lung cancer

  Relevance score: 0.00

  Shuyin Duan, Huimin Cao, Hong Liu, Lijun Miao, Jing Wang, Xiaolei Zhou, Wei Wang, Pingzhao Hu, Lingbo Qu, Yongjun Wu

  Keywords: machine learning, lung cancer, multidimensional variables, multimode diagnosis

  Published in Aging on May 23, 2020

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• Research PaperVolume 9, Issue 7pp 1721-1737

  Machine learning for predicting lifespan-extending chemical compounds

  Relevance score: 0.00

  Diogo G. Barardo, Danielle Newby, Daniel Thornton, Taravat Ghafourian, João Pedro de Magalhães, Alex A. Freitas

  Keywords: longevity, anti-ageing drugs, pharmaceutical interventions, ageing, bioinformatics, C. elegans, machine learning

  Published in Aging on July 18, 2017

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• Research PaperVolume 16, Issue 5pp 4654-4669

  Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

  Relevance score: 0.00

  Lei Shu, Hua Yan, Yanze Wu, Tengfeng Yan, Li Yang, Si Zhang, Zhihao Chen, Qiuye Liao, Lu Yang, Bing Xiao, Minhua Ye, Shigang Lv, Miaojing Wu, Xingen Zhu, Ping Hu

  Keywords: aneurysmal subarachnoid hemorrhage, high-grade, explainable machine learning, SHapley additive exPlanations, prognosis prediction

  Published in Aging on March 1, 2024

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• Research PaperVolume 12, Issue 21pp 21481-21503

  Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients

  Relevance score: 0.00

  Kui Chen, Bingsong Huang, Shan Yan, Siyi Xu, Keqin Li, Kuiming Zhang, Qi Wang, Zhongwei Zhuang, Liang Wei, Yanfei Zhang, Min Liu, Hao Lian, Chunlong Zhong

  Keywords: medulloblastoma, machine learning, prognostic model, overall survival

  Published in Aging on November 5, 2020

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• CorrectionVolume 17, Issue 2pp 608-608

  Correction for: Machine learning-based B cell-related diagnostic biomarker signature and molecular subtypes characteristic of ulcerative colitis

  Relevance score: 0.00

  Guo-Liang Wu, Li Li, Xiao-Yao Chen, Wei-Feng Zhang, Jun-Bo Wu, Xiaoning Yu, Hong-Jin Chen

  Keywords: ulcerative colitis, B cell-related genes, diagnostic biomarker, immune infiltration, machine learning

  Published in Aging on February 28, 2025

• Research PaperVolume 15, Issue 14pp 7146-7160

  Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment

  Relevance score: 0.00

  Yating Sun, Shengfu He, Mingyang Tang, Ding Zhang, Bao Meng, Jiawen Yu, Yanyan Liu, Jiabin Li

  Keywords: EMT, machine learning, WGCNA, HCC, prognosis

  Published in Aging on July 21, 2023

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• EditorialVolume 15, Issue 8pp 2822-2823

  Neuroimaging and machine learning for brain age estimation

  Relevance score: 0.00

  Thomas D. Kocar, Michael Denkinger, Jan Kassubek

  Keywords: brain age, machine learning, neuroimaging, magnetic resonance imaging, neurodegeneration

  Published in Aging on April 28, 2023