Suppr超能文献

衰老生物标志物公开竞赛。

An Open Competition for Biomarkers of Aging.

作者信息

Ying Kejun, Paulson Seth, Reinhard Julian, de Lima Camillo Lucas Paulo, Träuble Jakob, Jokiel Stefan, Gobel Dane, Herzog Chiara, Poganik Jesse R, Moqri Mahdi, Gladyshev Vadim N

机构信息

Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.

出版信息

bioRxiv. 2024 Nov 3:2024.10.29.620782. doi: 10.1101/2024.10.29.620782.

Abstract

Open scientific competitions have successfully driven biomedical advances but remain underutilized in aging research, where biological complexity and heterogeneity require methodological innovations. Here, we present the results from Phase I of the Biomarkers of Aging Challenge, an open competition designed to drive innovation in aging biomarker development and validation. The challenge leverages a unique DNA methylation dataset and aging outcomes from 500 individuals, aged 18 to 99. Participants are asked to develop novel models to predict chronological age, mortality, and multi-morbidity. Results from the chronological age prediction phase show important advances in biomarker accuracy and innovation compared to existing models. The winning models feature improved predictive power and employ advanced machine learning techniques, innovative data preprocessing, and the integration of biological knowledge. These approaches have led to the identification of novel age-associated methylation sites and patterns. This challenge establishes a paradigm for collaborative aging biomarker development, potentially accelerating the discovery of clinically relevant predictors of aging-related outcomes. This supports personalized medicine, clinical trial design, and the broader field of geroscience, paving the way for more targeted and effective longevity interventions.

摘要

开放式科学竞赛成功推动了生物医学的进步,但在衰老研究中仍未得到充分利用,因为衰老研究中的生物复杂性和异质性需要方法创新。在此,我们展示了衰老生物标志物挑战赛第一阶段的结果,这是一项旨在推动衰老生物标志物开发和验证创新的开放式竞赛。该挑战赛利用了一个独特的DNA甲基化数据集以及来自500名年龄在18至99岁之间个体的衰老结果。参与者被要求开发新模型来预测实际年龄、死亡率和多种疾病。实际年龄预测阶段的结果表明,与现有模型相比,生物标志物在准确性和创新性方面取得了重要进展。获胜模型具有更高的预测能力,采用了先进的机器学习技术、创新的数据预处理方法以及生物知识的整合。这些方法已导致识别出与年龄相关的新型甲基化位点和模式。这项挑战赛为协作式衰老生物标志物开发建立了一个范例,有可能加速发现与衰老相关结果的临床相关预测指标。这为个性化医疗、临床试验设计以及更广泛的老年科学领域提供了支持,为更具针对性和有效性的长寿干预措施铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01d5/11565782/c222be1e149b/nihpp-2024.10.29.620782v1-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验