{"id":"W4412163872","doi":"10.1158/1557-3265.aimachine-a022","title":"Abstract A022: Deep Learning Meets Transcriptomics: CNN-1D Powered Interpretation of probe based transcriptome array in Prostate Cancer","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transcriptome; Prostate cancer; Artificial intelligence; Interpretation (philosophy); Computational biology; Deep learning; Computer science; Cancer; Biology; Medicine; Bioinformatics; Internal medicine; Gene expression; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005789409,0.0007721423,0.0003218926,0.0004240109,0.0001865442,0.0007477276,0.0009105904,0.0006812042,0.002575548],"category_scores_gemma":[0.00134892,0.0003327813,0.0006997889,0.0003564855,0.0003191089,0.0004350162,0.0006372748,0.0007855135,0.0006030274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000947077,"about_ca_system_score_gemma":0.0009395727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009993618,"about_ca_topic_score_gemma":0.01020118,"domain_scores_codex":[0.9998329,0.00003657939,0.00000553004,0.00005236543,0.00004891358,0.00002375024],"domain_scores_gemma":[0.9997901,0.00008947311,0.00001996761,0.00003029699,0.00004967904,0.00002044045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003828151,0.0001418416,0.007905646,0.0002309988,0.0001568629,0.0003512838,0.0000815512,0.7287818,0.0630994,0.006011179,0.012091,0.1807657],"study_design_scores_gemma":[0.000004522651,0.00001676866,0.0005018486,0.000004838769,0.000005450495,0.00002025497,0.000005443239,0.9931083,0.004381939,0.001274383,0.0006703112,0.000005891065],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1889073,0.001201567,0.786379,0.002070672,0.0002567209,0.0001378273,0.004027197,0.01028982,0.006729765],"genre_scores_gemma":[0.7802688,0.0005228856,0.2082044,0.0006617727,0.0000720834,0.0002003304,0.0035203,0.0004540652,0.006095172],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009993618,"threshold_uncertainty_score":0.01987088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04670089997177013,"score_gpt":0.4586530007547375,"score_spread":0.4119521007829673,"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."}}