{"id":"W2153848946","doi":"10.1038/srep04092","title":"Importance of collection in gene set enrichment analysis of drug response in cancer cell lines","year":2014,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; Université de Montréal; Montreal Clinical Research Institute","funders":"","keywords":"Leverage (statistics); Context (archaeology); Computational biology; Cancer cell lines; Gene; Computer science; Biology; Bioinformatics; Cancer; Genetics; Cancer cell; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001932424,0.00007025533,0.0001919298,0.000185091,0.00002367887,0.00001076415,0.00007103462,0.00005467888,0.00001618641],"category_scores_gemma":[0.00004402807,0.00006573946,0.00007561887,0.0007281657,0.00006289013,0.000003688074,0.00004664111,0.00003310879,1.428562e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000304643,"about_ca_system_score_gemma":0.0001097342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002651362,"about_ca_topic_score_gemma":0.003171926,"domain_scores_codex":[0.9988308,0.00005341743,0.0005904792,0.0002641474,0.000132641,0.0001285278],"domain_scores_gemma":[0.9990411,0.00001173215,0.0004015689,0.0004311163,0.00008537045,0.00002908389],"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":[0.0002049586,0.0001075645,0.1991751,0.00003835822,0.00006863585,0.000005573051,0.0006324937,0.0244652,0.7713575,0.000002932286,0.003486816,0.0004548454],"study_design_scores_gemma":[0.0003996472,0.0001056209,0.109269,0.00002835877,0.0001016226,0.000005178394,0.0002123364,0.02340455,0.8611892,0.0002296963,0.00483926,0.0002155168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983957,0.0005294544,0.0002166682,0.00002122759,0.0005069034,0.0001136364,0.000005645831,0.000001351751,0.0002094342],"genre_scores_gemma":[0.9985207,0.00007151088,0.0003539294,0.00001591262,0.00001533725,0.0000121339,0.00008979446,0.000003863823,0.0009167755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08990613,"threshold_uncertainty_score":0.2680777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006955094259820967,"score_gpt":0.2491874023130614,"score_spread":0.2422323080532404,"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."}}