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Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry
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SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection From Protein Sequences.

Hanli Jiang1,2,3,4, Dongliang Deng5, Yu Yuan6,7

  • 1Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

Aging Cell
|December 23, 2025
PubMed
Summary

SenSeqNet, a new deep learning tool, accurately predicts cellular senescence from protein sequences. This AI framework accelerates research into aging and age-related disease therapeutics.

Keywords:
cellular senescencedeep learningprotein language model

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Gerontology

Background:

  • Cellular senescence is a key driver of aging and age-related diseases.
  • Accurate detection of senescence is crucial for understanding aging and developing therapies.
  • Current methods for detecting senescence are often slow and difficult to scale.

Purpose of the Study:

  • To develop a novel deep learning framework, SenSeqNet, for predicting cellular senescence directly from protein sequences.
  • To improve the efficiency and scalability of senescence detection.

Main Methods:

  • SenSeqNet integrates Evolutionary Scale Modeling (ESM-2) embeddings with a hybrid LSTM-CNN architecture.
  • The model analyzes protein sequences to capture sequential and structural features relevant to senescence.

Main Results:

  • SenSeqNet achieved 86.43% accuracy in independent testing, surpassing traditional machine learning and deep learning methods.
  • High-confidence genes predicted by SenSeqNet were significantly enriched in senescence-associated pathways, confirming biological relevance.

Conclusions:

  • SenSeqNet offers a robust, biologically informed tool for detecting cellular senescence.
  • This AI framework can accelerate research into aging mechanisms and the development of anti-aging therapeutics.