SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection From Protein Sequences.

ABSTRACT: 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 lear

SUBMITTER: Jiang H 

PROVIDER: S-EPMC12724000 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature