{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006732427,0.0001349831,0.0003037327,0.0001234584,0.0001273885,0.000174424,0.0009340562,0.00008830534,0.000001205026],"category_scores_gemma":[0.0006463041,0.0001101253,0.00001394318,0.001403782,0.0004666491,0.0002710899,0.00349246,0.00008308773,2.502582e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001303861,"about_ca_system_score_gemma":0.0021674,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001225537,"about_ca_topic_score_gemma":0.02504645,"domain_scores_codex":[0.9987603,0.00008777531,0.0004717216,0.0003639972,0.000115398,0.000200835],"domain_scores_gemma":[0.9983842,0.0002032828,0.0003979236,0.000505875,0.0004514377,0.00005726656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003432732,0.0008838378,0.5338176,0.000607068,0.0001186883,0.000003509916,0.005467901,0.000148785,0.01911057,0.00162966,0.00470004,0.4300796],"study_design_scores_gemma":[0.006056407,0.003567544,0.501366,0.0005420976,0.0000949687,0.000008514678,0.006633971,0.001372565,0.02039591,0.0003410427,0.4590636,0.0005573916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931896,0.0008595633,0.00001070369,0.0002299962,0.0001957498,0.0008630603,0.002329173,0.000002378902,0.002319783],"genre_scores_gemma":[0.9938532,0.004525974,0.0005218185,0.0004991777,0.00002600674,0.00002838606,0.000511284,0.000006272577,0.0000278884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4543635,"threshold_uncertainty_score":0.9927439,"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."}}