{"id":"W4404006896","doi":"10.1101/2024.10.28.620702","title":"SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Senescence; Deep learning; Computational biology; Artificial intelligence; Cellular senescence; Computer science; Biology; Cell biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007179618,0.0008993256,0.0007084568,0.0007229721,0.0002248269,0.0006763009,0.00156768,0.001104268,0.001975347],"category_scores_gemma":[0.001121553,0.0003993913,0.0007560538,0.0005960905,0.0004020621,0.0008320831,0.0008173247,0.001171609,0.0007265024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009911422,"about_ca_system_score_gemma":0.001008215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005396964,"about_ca_topic_score_gemma":0.008436543,"domain_scores_codex":[0.999803,0.00003970514,0.00001063294,0.00006742473,0.00005215599,0.00002702325],"domain_scores_gemma":[0.9997593,0.00008264134,0.00003102661,0.00002363362,0.00007901825,0.00002449713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004440156,0.0002382179,0.003352346,0.0003441905,0.0001966969,0.0002483387,0.00007235013,0.6752959,0.02801679,0.01163421,0.01633676,0.2638201],"study_design_scores_gemma":[0.000007174607,0.0000258583,0.0001057161,0.000004720297,0.000004180787,0.00001340324,0.000003405594,0.9947938,0.00185737,0.002455681,0.0007244477,0.000004115695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03762244,0.001196424,0.9503799,0.0004206449,0.0001371761,0.00008616093,0.001729478,0.007199187,0.001228607],"genre_scores_gemma":[0.4480878,0.001305086,0.5299643,0.000940013,0.0001378425,0.0004204207,0.008043407,0.000589624,0.01051163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005396964,"threshold_uncertainty_score":0.0107311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007835772416357877,"score_gpt":0.2294762497900664,"score_spread":0.2216404773737085,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}