{"id":"W2083753436","doi":"10.1371/journal.pone.0115054","title":"A Computational Strategy to Select Optimized Protein Targets for Drug Development toward the Control of Cancer Diseases","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Interactome; Transcriptome; Breast cancer; Cancer; Computational biology; Biology; Drug development; Interaction network; Drug discovery; Bioinformatics; Drug; Gene; Drug repositioning; Cancer cell; Cancer research; Gene expression; Genetics; Pharmacology","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":[0.0007284807,0.001002614,0.001204044,0.001204409,0.0005238685,0.0008952675,0.0009759506,0.0009123199,0.003565795],"category_scores_gemma":[0.001921954,0.0006182,0.001123486,0.0008722192,0.0004256861,0.0005799971,0.0007157642,0.0007003649,0.0005430924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000622771,"about_ca_system_score_gemma":0.001815254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003226236,"about_ca_topic_score_gemma":0.00454425,"domain_scores_codex":[0.9998037,0.00006938016,0.00001216228,0.00003932245,0.00004679741,0.0000286449],"domain_scores_gemma":[0.9995233,0.0003040646,0.00003441121,0.00003631907,0.00007352611,0.00002841345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001679052,0.000126975,0.001848661,0.0001677834,0.0001377241,0.0001593173,0.00003760396,0.9513817,0.003870252,0.008260243,0.001930046,0.03191181],"study_design_scores_gemma":[0.0000324931,0.0000440279,0.0001438809,0.00000644018,0.00003370714,0.00001858427,0.00001175815,0.9939732,0.0007197213,0.004047111,0.0009640562,0.000005070444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1049349,0.0006656151,0.878264,0.0008663831,0.00009696309,0.0003493597,0.001071352,0.002347796,0.01140363],"genre_scores_gemma":[0.429882,0.0005060085,0.5617933,0.0004658697,0.00006519233,0.001248033,0.00182528,0.0003318769,0.003882455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003565795,"threshold_uncertainty_score":0.01192874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700366559945269,"score_gpt":0.2449966516526141,"score_spread":0.2179929860531614,"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."}}