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• Research PaperVolume 4, Issue 7pp 499-508

  The lifespan extension effects of resveratrol are conserved in the honey bee and may be driven by a mechanism related to caloric restriction

  Relevance score: 0.00

  Brenda Rascón, Basil P. Hubbard, David A. Sinclair, Gro V. Amdam

  Keywords: hyperoxia, learning performance, aging, lifespan, Apis mellifera, resveratrol

  Published in Aging on July 31, 2012

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• EditorialVolume 15, Issue 19pp 9894-9895

  Learning by motor imagery in older adults

  Relevance score: 0.00

  Arnaud Saimpont, Angèle Métais, Christian Collet

  Keywords: motor imagery, motor sequence learning (MSL), tDCS, aging

  Published in Aging on October 13, 2023

• Research PaperVolume 13, Issue 11pp 15320-15335

  Cistanche deserticola polysaccharides alleviate cognitive decline in aging model mice by restoring the gut microbiota-brain axis

  Relevance score: 0.00

  Yuan Gao, Bing Li, Hong Liu, Yajuan Tian, Chao Gu, Xiaoli Du, Ren Bu, Jie Gao, Yang Liu, Gang Li

  Keywords: learning and memory deterioration, CDPS, gut microbiota

  Published in Aging on June 3, 2021

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• ReviewVolume 17, Issue 1pp 251-275

  Deep learning and generative artificial intelligence in aging research and healthy longevity medicine

  Relevance score: 0.00

  Dominika Wilczok

  Keywords: aging, generative artificial intelligence, deep learning, deep aging clocks, healthy longevity medicine

  Published in Aging on January 16, 2025

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• 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 8, Issue 5pp 1021-1030

  Deep biomarkers of human aging: Application of deep neural networks to biomarker development

  Relevance score: 0.00

  Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, Alex Zhavoronkov

  Keywords: deep learning, deep neural networks, biomarker development, aging biomarkers, human aging, machine learning

  Published in Aging on May 18, 2016

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