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Articles by Adiv A. Johnson
Explainable epigenetic aging clocks: an overview of existing AI models and approaches
ABSTRACT INTRODUCTION There is a growing demand to make epigenetic aging clocks that are interpretable and actionable. Here, we identify and describe explainable artificial intelligence (XAI) epigenetic clocks that convert CpGs into higher-level abstractions using deep learning. AREAS COVERED By combing through PubMed, this narrative review identifies six different XAI clocks: XAI-AGE, DL-XAI, EpInflammAge, PathwayAge, EXP.REACTOME, and EXP.TFT.
Turning back time: a comprehensive list of interventions that decrease next-generation epigenetic aging clocks in humans
Abstract Epigenetic aging clocks estimate age from DNA methylation patterns and have become central tools in longevity research. More recently, next-generation clocks have been developed to better compensate for the known divergence between chronological age and epigenetic age in ways that relate to lifestyle, health, and age-related disease.
Contextualizing aging clocks and properly describing biological age
Usage of the phrase “biological age” has become increasingly common among biomedical scientists, as evinced by the increased prevalence of this term over time (Figure 1). The earliest record indexed in PubMed dates back to 1949 and, although a slight increase was observed in the late 20th century, this terminology really picks up in the second decade of the 21st century (Figure 1).
Human age reversal: Fact or fiction?
1.5 Outstanding questions and limitations in the field In this work, we refer to the measurement made by an aging clock as BA given that the disparity between BA and CA significantly correlates with age-related health outcomes such as mortality (Perna et al., 2016) and disease burden (Hillary et al., 2020). Whether or not the metric provided by an aging clock truly represents BA is, however, debatable.
Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease
Introduction In 2013, both Horvath1 and Hannum et al.2 utilized a panel of methylated CpG sites in conjunction with a regression model to predict human age.
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