{"id":"W4399504458","doi":"10.1158/1538-8514.synthleth24-b013","title":"Abstract B013: Deep learning-based prediction of synthetic essentialities in <i>CTNNB1</i>-mutated hepatocellular carcinoma","year":2024,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gene; Hepatocellular carcinoma; Context (archaeology); Computational biology; Biology; Synthetic lethality; Bioinformatics; Genetics; DNA repair","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":[0.0006446925,0.0006959686,0.0004412769,0.0007529943,0.0002069283,0.0004843047,0.0005520941,0.0005870436,0.001782269],"category_scores_gemma":[0.001537917,0.0002203161,0.0006007147,0.0004224684,0.0002228974,0.0002703691,0.0005267326,0.0007585449,0.0004784479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007785329,"about_ca_system_score_gemma":0.0009513072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009288952,"about_ca_topic_score_gemma":0.007976694,"domain_scores_codex":[0.9998114,0.00004162558,0.00001279718,0.0000578221,0.00003793718,0.00003852918],"domain_scores_gemma":[0.9995151,0.000252371,0.00004545119,0.0000347237,0.0001024232,0.00005002674],"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":[0.002423893,0.0009194733,0.1618038,0.0006971908,0.0006145661,0.0009736585,0.00008409665,0.5861182,0.04105066,0.001737797,0.02424453,0.1793321],"study_design_scores_gemma":[0.00004002808,0.0001664856,0.008368992,0.00001953507,0.00005448851,0.00009629372,0.00001991442,0.9808711,0.00829377,0.00102407,0.001033394,0.00001191423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9268562,0.001381152,0.05570969,0.0009617471,0.00009232645,0.0001023678,0.01004374,0.002064031,0.002788765],"genre_scores_gemma":[0.9625982,0.0003264122,0.0173325,0.0001860276,0.00002627702,0.00008947125,0.0174858,0.00005815634,0.001897163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009288952,"threshold_uncertainty_score":0.01846981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011712332517158,"score_gpt":0.2354377841799214,"score_spread":0.2237254516627634,"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."}}