{"id":"W1794482832","doi":"10.1089/omi.2012.0005","title":"Prioritizing Cancer Therapeutic Small Molecules by Integrating Multiple OMICS Datasets","year":2012,"lang":"en","type":"article","venue":"OMICS A Journal of Integrative Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"DrugBank; Toxicogenomics; Drug discovery; Computer science; Computational biology; Cancer; Omics; Genomics; Gene selection; Bioinformatics; Drug; Biology; Gene; Pharmacology; Microarray analysis techniques; Genome; Genetics","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":[0.00114313,0.0002536101,0.0004231462,0.0001834219,0.0001034722,0.00009997654,0.0009757628,0.0001093512,0.000004943483],"category_scores_gemma":[0.0006401619,0.0001740281,0.000157949,0.0002516418,0.0001255851,0.0005785102,0.0002347662,0.0005354753,0.000003652054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002386769,"about_ca_system_score_gemma":0.0002785191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008637832,"about_ca_topic_score_gemma":0.0000395063,"domain_scores_codex":[0.9980274,0.0006123758,0.000608724,0.0002289029,0.0001362179,0.000386342],"domain_scores_gemma":[0.9972402,0.001369467,0.0006701369,0.0002289259,0.000340096,0.0001512073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002244446,0.000434864,0.01719053,0.00002181496,0.0005837015,0.00001568072,0.006542756,0.0008492219,0.3888049,0.2872436,0.001478387,0.2966101],"study_design_scores_gemma":[0.004553678,0.001869687,0.01117809,0.0005968341,0.0002181401,0.001333644,0.00474355,0.5755597,0.2280625,0.06938042,0.1001879,0.002315815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233957,0.005387826,0.8690487,0.0006699821,0.001089005,0.000104276,0.0001896442,0.00001500387,0.0000999417],"genre_scores_gemma":[0.6631967,0.0004075863,0.3351935,0.000829607,0.00028139,0.000008650701,0.00005245737,0.00001819849,0.00001194248],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5747105,"threshold_uncertainty_score":0.7096661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0400914198172459,"score_gpt":0.3481435112644947,"score_spread":0.3080520914472488,"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."}}