Correction to "An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging".

IF 8 1区医学 Q1 CELL BIOLOGY

Aging Cell Pub Date : 2024-10-08 DOI:10.1111/acel.14363

引用次数: 0

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对 "基于机器学习的可解释脑脊液蛋白质组学预测年龄时钟揭示了大脑衰老的新见解 "的更正。

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来源期刊

Aging Cell Biochemistry, Genetics and Molecular Biology-Cell Biology

自引率

2.60%

发文量

212

期刊介绍： Aging Cell is an Open Access journal that focuses on the core aspects of the biology of aging, encompassing the entire spectrum of geroscience. The journal's content is dedicated to publishing research that uncovers the mechanisms behind the aging process and explores the connections between aging and various age-related diseases. This journal aims to provide a comprehensive understanding of the biological underpinnings of aging and its implications for human health. The journal is widely recognized and its content is abstracted and indexed by numerous databases and services, which facilitates its accessibility and impact in the scientific community. These include: Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) Biological Science Database (ProQuest) CAS: Chemical Abstracts Service (ACS) Embase (Elsevier) InfoTrac (GALE Cengage) Ingenta Select ISI Alerting Services Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) Natural Science Collection (ProQuest) PubMed Dietary Supplement Subset (NLM) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) Web of Science (Clarivate Analytics) Being indexed in these databases ensures that the research published in Aging Cell is discoverable by researchers, clinicians, and other professionals interested in the field of aging and its associated health issues. This broad coverage helps to disseminate the journal's findings and contributes to the advancement of knowledge in geroscience.