{"id":"W4250616341","doi":"10.1177/117693510700500005","title":"Perturbation of Interaction Networks for Application to Cancer Therapy","year":2007,"lang":"en","type":"article","venue":"Cancer Informatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Interaction network; Biological network; Computational biology; Computer science; Cancer; Protein–protein interaction; Drug target; Protein Interaction Networks; Prostate cancer; Systems biology; Cancer cell; Drug discovery; Bioinformatics; Biology; Gene; Cancer research; 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":[0.0002439748,0.00009633792,0.0001050592,0.00004276582,0.00004264666,0.0000135065,0.0001166954,0.0001159134,0.00000983091],"category_scores_gemma":[0.00001009576,0.00008776768,0.00005586718,0.00009432986,0.0000194185,0.00001098985,0.00002731656,0.00005078319,0.000002075397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003952712,"about_ca_system_score_gemma":0.00004440331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001813823,"about_ca_topic_score_gemma":0.00006648145,"domain_scores_codex":[0.999278,0.000003189647,0.0004077983,0.00006643005,0.00007109406,0.0001734556],"domain_scores_gemma":[0.9993333,0.0000158347,0.0002348741,0.0001794965,0.0001785214,0.00005803538],"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":[0.0006662133,0.00004018901,0.0009615364,0.0001299381,0.0001010188,1.949121e-8,0.001815825,0.1170351,0.02341587,0.0009548585,0.01608155,0.8387979],"study_design_scores_gemma":[0.001468606,0.0005006132,0.001517786,0.00008319268,0.00003561542,0.000002502821,0.001022635,0.2155349,0.1121879,0.0003046987,0.6668819,0.0004595818],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09332766,0.0005808059,0.9038131,0.00008327462,0.000396793,0.0005876169,0.00002793853,0.000006607489,0.001176221],"genre_scores_gemma":[0.9906594,0.001092745,0.005341719,0.001638408,0.0005910128,0.0001743285,0.0001584986,0.00001644691,0.0003274303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8984714,"threshold_uncertainty_score":0.3579062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336000474826323,"score_gpt":0.3034665275344232,"score_spread":0.29010652278616,"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."}}