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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 PMCID: PMC11342017 DOI: 10.2196/57830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Chen X, Deng M, Wang Z, Huang C. MMP3C: an in-silico framework to depict cancer metabolic plasticity using gene expression profiles. Brief Bioinform 2023; 25:bbad471. [PMID: 38145946 PMCID: PMC10749788 DOI: 10.1093/bib/bbad471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/08/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Metabolic plasticity enables cancer cells to meet divergent demands for tumorigenesis, metastasis and drug resistance. Landscape analysis of tumor metabolic plasticity spanning different cancer types, in particular, metabolic crosstalk within cell subpopulations, remains scarce. Therefore, we proposed a new in-silico framework, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict tumor metabolic plasticity based on transcriptome data. Next, we performed an extensive metabo-plastic analysis of over 6000 tumors comprising 13 cancer types. The metabolic plasticity within distinct cell subpopulations, particularly interplay with tumor microenvironment, were explored at single-cell resolution. Ultimately, the metabo-plastic events were screened out for multiple clinical applications via machine learning methods. The pilot research indicated that 6 out of 13 cancer types exhibited signs of the Warburg effect, implying its high reliability and robustness. Across 13 cancer types, high metabolic organized heterogeneity was found, and four metabo-plastic subtypes were determined, which link to distinct immune and metabolism patterns impacting prognosis. Moreover, MMP3C analysis of approximately 60 000 single cells of eight breast cancer patients unveiled several metabo-plastic events correlated to tumorigenesis, metastasis and immunosuppression. Notably, the metabolic features screened out by MMP3C are potential biomarkers for diagnosis, tumor classification and prognosis. MMP3C is a practical cross-platform tool to capture tumor metabolic plasticity, and our study unveiled a core set of metabo-plastic pairs among diverse cancer types, which provides bases toward improving response and overcoming resistance in cancer therapy.
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Affiliation(s)
- Xingyu Chen
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao SAR 999078, China
| | - Min Deng
- CRDA, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR 999078, China
| | - Zihan Wang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao SAR 999078, China
| | - Chen Huang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao SAR 999078, China
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