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• W4366998610 abstract "Abstract Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk." @default.
• W4366998610 created "2023-04-27" @default.
• W4366998610 creator A5015783903 @default.
• W4366998610 creator A5036512342 @default.
• W4366998610 creator A5043144258 @default.
• W4366998610 creator A5055461749 @default.
• W4366998610 creator A5066218248 @default.
• W4366998610 creator A5071039477 @default.
• W4366998610 creator A5085526125 @default.
• W4366998610 creator A5085862348 @default.
• W4366998610 date "2023-04-25" @default.
• W4366998610 modified "2023-10-18" @default.
• W4366998610 title "Decoding the role of transcriptomic clocks in the human prefrontal cortex" @default.
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