{"id":"W4361943883","doi":"10.1158/1078-0432.c.6517912","title":"Data from Tissue Targeting in Cancer: eIF4E's Tale","year":2023,"lang":"en","type":"preprint","venue":"","topic":"PI3K/AKT/mTOR signaling in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Institutes of Health; Leukemia and Lymphoma Society","keywords":"EIF4E; Agonist; Cancer research; Ovarian cancer; Translation (biology); Cancer; Receptor; Peptide; Protein biosynthesis; Biology; Internal medicine; Endocrinology; Molecular biology; Medicine; Messenger RNA; Gene; Biochemistry","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":[0.001172064,0.001037961,0.0009237397,0.00167585,0.0006264091,0.001808829,0.001097277,0.001527854,0.08291241],"category_scores_gemma":[0.001614151,0.0004330459,0.001018706,0.001751271,0.0005099421,0.001296037,0.001340676,0.003078436,0.04680314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009430867,"about_ca_system_score_gemma":0.0008558733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588946,"about_ca_topic_score_gemma":0.002381938,"domain_scores_codex":[0.9994684,0.00004570647,0.00003308972,0.00008341143,0.000283934,0.00008549116],"domain_scores_gemma":[0.9991094,0.0001651939,0.00004260923,0.0001677654,0.0002422718,0.0002726906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006829483,0.0001115716,0.0004971989,0.001437813,0.0001405859,0.0002804043,0.0001033991,0.0008696615,0.03196238,0.02098992,0.7876173,0.155307],"study_design_scores_gemma":[0.00006442977,0.00006217336,0.001340474,0.0001061966,0.00005599411,0.0002025561,0.00002783501,0.0003684551,0.01221203,0.008305714,0.9772283,0.00002579432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.02210906,0.1381209,0.09518009,0.111163,0.06411711,0.0004700525,0.1455811,0.01630023,0.4069586],"genre_scores_gemma":[0.1150226,0.1263334,0.06725758,0.02302076,0.01075982,0.0007782981,0.1522723,0.006171789,0.4983836],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.08291241,"threshold_uncertainty_score":0.2773697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08076619494627217,"score_gpt":0.3692001107583033,"score_spread":0.2884339158120311,"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."}}