{"id":"W4404237122","doi":"10.1093/neuonc/noae165.0527","title":"DDDR-42. LEVERAGING GENOME-WIDE CRISPR/CAS9 KNOCKOUT DRUG SCREENS TO IDENTIFY SENSITIZERS FOR PROTEOSOME INHIBITORS IN GLIOBLASTOMA","year":2024,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"CRISPR; Glioblastoma; Drug discovery; Computational biology; Drug; Genome; Drug target; Cancer research; Biology; Genetics; Pharmacology; Bioinformatics; 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":[0.0002460205,0.0004589256,0.0005876477,0.000401267,0.0002066659,0.0004728475,0.0003273408,0.0004052047,0.001414274],"category_scores_gemma":[0.0001454329,0.0002246521,0.000373509,0.0002521158,0.0002167992,0.0001383641,0.0002565951,0.0005308894,0.000451099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004313969,"about_ca_system_score_gemma":0.0003268602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001545335,"about_ca_topic_score_gemma":0.003594022,"domain_scores_codex":[0.9997501,0.00002360012,0.00003332596,0.00006162106,0.00009530314,0.000036013],"domain_scores_gemma":[0.9999194,0.00001222572,0.00002833342,0.000009875213,0.00001242583,0.00001768504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005705199,0.00003977136,0.0001984868,0.00005930285,0.00001098449,0.00007955444,0.000009187883,0.0003222382,0.9961592,0.0001001524,0.0001455178,0.002818641],"study_design_scores_gemma":[0.00002514558,0.000365846,0.001918259,0.00001031403,0.00003489104,0.0004451435,0.00002025569,0.002286757,0.9884955,0.00005959874,0.006326029,0.00001234576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9517682,0.004397772,0.02816826,0.0005277462,0.0001524739,0.0004938713,0.00666939,0.001674634,0.006147553],"genre_scores_gemma":[0.9701779,0.002193998,0.01842334,0.0002450432,0.00001028604,0.0001879317,0.003130817,0.0001471139,0.005483614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001545335,"threshold_uncertainty_score":0.004731178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497685841960041,"score_gpt":0.3048440016401705,"score_spread":0.2898671432205701,"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."}}