{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01895935,0.001140503,0.003349753,0.004069766,0.002603482,0.005773,0.002465593,0.000912672,0.001632823],"category_scores_gemma":[0.04403754,0.0006617554,0.003118013,0.006561436,0.002310132,0.002915464,0.00390895,0.002715588,0.000737722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601544,"about_ca_system_score_gemma":0.003762455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00412674,"about_ca_topic_score_gemma":0.009604811,"domain_scores_codex":[0.9788127,0.00978428,0.001783002,0.003509258,0.005230798,0.0008798818],"domain_scores_gemma":[0.9606324,0.02294062,0.001819021,0.009071374,0.004575158,0.0009615309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007264356,0.002196576,0.3211046,0.006588276,0.01151237,0.001326498,0.002572951,0.07624988,0.1862742,0.01525499,0.03263214,0.3370232],"study_design_scores_gemma":[0.0009102898,0.002751066,0.4480174,0.001395819,0.006744894,0.002443495,0.003403622,0.0979602,0.2232723,0.05857509,0.1534744,0.00105129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7464684,0.006795566,0.1939156,0.002019677,0.0006424635,0.001623562,0.03388837,0.005470015,0.009176302],"genre_scores_gemma":[0.6887502,0.002221068,0.2246539,0.001131304,0.0002157806,0.002335641,0.07617567,0.002638815,0.001877625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01895935,"threshold_uncertainty_score":0.1002679,"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."}}