{"id":"W4399505156","doi":"10.1158/1538-8514.synthleth24-ia011","title":"Abstract IA011: Towards mapping the landscape of cancer vulnerabilities","year":2024,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer; Resource (disambiguation); Dependency (UML); Citation; Computational biology; Cancer research; Biology; Computer science; Library science; Genetics; Artificial intelligence","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":[0.001585736,0.000822558,0.000619347,0.002918946,0.0006689592,0.002603487,0.0009129054,0.0007130645,0.01004843],"category_scores_gemma":[0.007563027,0.0004531807,0.0009932393,0.002257018,0.0006520806,0.001310869,0.002622612,0.001346215,0.001860772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207508,"about_ca_system_score_gemma":0.002099496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006291947,"about_ca_topic_score_gemma":0.005864368,"domain_scores_codex":[0.9993739,0.0002653452,0.00002062654,0.0001436417,0.0001561111,0.00004050995],"domain_scores_gemma":[0.9978889,0.001172086,0.0001948589,0.0002683127,0.0003113411,0.0001645322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005172362,0.0001574348,0.04767414,0.001290385,0.0003333009,0.0003443595,0.0003756133,0.290472,0.008846824,0.1116138,0.2019593,0.3364157],"study_design_scores_gemma":[0.00008315944,0.0001456699,0.01923037,0.0002519896,0.0001315507,0.0003617797,0.0004170449,0.5564892,0.007017528,0.2419372,0.1738525,0.00008199143],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1198552,0.005067467,0.6975524,0.01676564,0.0004624172,0.0005727849,0.09457934,0.0135347,0.05161009],"genre_scores_gemma":[0.4479015,0.005180025,0.4716627,0.001922382,0.0002390781,0.0008520807,0.06116856,0.001892167,0.00918156],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01004843,"threshold_uncertainty_score":0.03361529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05732427462323225,"score_gpt":0.3432844338601982,"score_spread":0.2859601592369659,"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."}}