SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences
Bibliographic record
Abstract
Abstract Cellular senescence, characterized by the irreversible cessation of division in normally proliferating cells due to various stressors, presents a significant challenge in the treatment of age-related diseases. Understanding and accurately detecting cellular senescence is crucial for identifying potential therapeutic targets. However, traditional wet lab assays for detecting cellular senescence are time-consuming and labor-intensive, limiting research and drug development efficiency. There is an urgent need for computational tools allowing swift and accurate detection of cellular senescence from protein sequences. We propose SenSeqNet, a novel deep learning framework for detecting cellular senescence directly from protein sequences. The framework begins with feature extraction using the Evolutionarily Scaled Model (ESM-2), a state-of-the-art protein language model that captures evolutionary information and complex sequence patterns. The extracted embeddings are then passed through a hybrid architecture consisting of long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to further refine and learn from the embedded information. SenSeqNet achieved a final accuracy of 83.55% on independent testing, surpassing various machine learning and deep learning architectures. This performance underscoring the robustness and effectiveness of SenSeqNet for detecting cellular senescence from protein sequences. These results provide a solid foundation for future research on aging and age-related therapeutics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".