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SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection From Protein Sequences
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DOI：10.1111/acel.70344.png)
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Abstract
En 中文
Cellular senescence, defined as the irreversible arrest of cell proliferation in response to stress, contributes to tissue dysfunction and drives the progression of age-related diseases. Accurate detection of senescent states is therefore essential for understanding aging mechanisms and identifying therapeutic targets. However, conventional laboratory assays are time-consuming and difficult to scale. Here, we present SenSeqNet, a deep learning framework that predicts cellular senescence directly from protein sequences. SenSeqNet integrates embeddings from the Evolutionary Scale Modeling (ESM-2) with a hybrid LSTM-CNN architecture to capture both sequential and higher-order structural features. The model achieved 86.43% accuracy in independent testing, outperforming traditional machine learning and deep learning approaches. Importantly, the high-confidence genes predicted by SenSeqNet were significantly enriched in canonical senescence-associated pathways, indicating that the model captures biologically coherent regulatory programs rather than overfitting to sequence labels. These results establish SenSeqNet as a robust and biologically informed tool for senescence detection and provide a foundation for accelerating research into aging and age-related therapeutics.
Keywords:
cellular senescence
deep learning
protein language model
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