{"id":"W4406747600","doi":"10.1016/j.esmorw.2024.100097","title":"Collaborating across sectors in service of open science, precision oncology, and patients: an overview of the AACR Project GENIE (Genomics Evidence Neoplasia Information Exchange) Biopharma Collaborative (BPC)","year":2025,"lang":"en","type":"article","venue":"ESMO Real World Data and Digital Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Genentech; National Institutes of Health; Seagen; Gilead Sciences; National Comprehensive Cancer Network; Bayer HealthCare; Sanofi; Memorial Sloan-Kettering Cancer Center; GlaxoSmithKline; Amgen; Pfizer; American Association for Cancer Research; AstraZeneca; Eli Lilly and Company","keywords":"Precision oncology; Service (business); Genomics; Open science; Data exchange; Cancer; Medicine; Knowledge management; Computer science; Internal medicine; Business; World Wide Web; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0539889,0.00113256,0.0006146861,0.006641828,0.003300235,0.01403661,0.002613023,0.004650936,0.006862276],"category_scores_gemma":[0.01784908,0.001030862,0.0009543161,0.009952911,0.002805554,0.009751116,0.01506799,0.003798892,0.00343981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00926782,"about_ca_system_score_gemma":0.04681465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01968429,"about_ca_topic_score_gemma":0.02949714,"domain_scores_codex":[0.9619336,0.02286841,0.002177056,0.002992924,0.006006851,0.004021249],"domain_scores_gemma":[0.9731661,0.007259398,0.001815125,0.001842226,0.006465476,0.009451698],"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":[0.0001808163,0.0003299162,0.01832049,0.002615129,0.0001176755,0.0005148845,0.004560355,0.001238577,0.001137459,0.06760272,0.1333676,0.7700144],"study_design_scores_gemma":[0.00006275206,0.0002514124,0.01338994,0.005312354,0.00005802819,0.000896476,0.004699194,0.001096064,0.0005761888,0.01987054,0.953719,0.00006801156],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01844381,0.3150665,0.1253932,0.3712467,0.003874621,0.002939468,0.002654075,0.002094163,0.1582876],"genre_scores_gemma":[0.1595906,0.3318595,0.3637842,0.08127087,0.004154351,0.004309998,0.009615817,0.001737008,0.04367772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.997387,"threshold_uncertainty_score":0.285524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06257212354774677,"score_gpt":0.4189127545338366,"score_spread":0.3563406309860899,"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."}}