{"id":"W1984650538","doi":"10.3389/fgene.2014.00152","title":"Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial","year":2014,"lang":"en","type":"review","venue":"Frontiers in Genetics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Health; Institut National Du Cancer; Institute of Cancer Research; Direction Générale de l’offre de Soins; Agence Nationale de la Recherche; European Commission","keywords":"Workflow; Precision medicine; Traceability; Computer science; Personalized medicine; Context (archaeology); Profiling (computer programming); Clinical trial; Data science; Bioinformatics; Medicine; Medical physics; Data mining; Computational biology; Software engineering; Biology; Pathology; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001534312,0.0002568067,0.0009983549,0.0001390847,0.0000428116,0.00001987817,0.0004417834,0.0005855486,5.691775e-7],"category_scores_gemma":[0.001052832,0.00018424,0.0001447792,0.0001469966,0.000133972,0.000001194969,0.0002569431,0.0002158817,0.000002031428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008238682,"about_ca_system_score_gemma":0.0003125947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006200818,"about_ca_topic_score_gemma":0.0001780181,"domain_scores_codex":[0.997853,0.0001462931,0.001224293,0.0004231858,0.0001080488,0.0002451515],"domain_scores_gemma":[0.9985615,0.0003116225,0.0003889372,0.0005864156,0.00004967883,0.0001018542],"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":[0.0005387394,0.00004163548,0.0001004261,0.0006022779,0.00002994632,2.319922e-7,0.00004356519,0.0001204831,6.482414e-7,0.00003211441,0.01927054,0.9792194],"study_design_scores_gemma":[0.004617922,0.0012922,0.00003466025,0.0004759354,0.0001396014,0.000002391668,0.00003359752,0.001398638,0.000002321886,0.0001252285,0.9916697,0.0002077964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001671587,0.894677,0.09915515,0.0002137321,0.001727988,0.003800949,0.00007150381,0.000003234175,0.0001832665],"genre_scores_gemma":[0.00002796505,0.9656998,0.031599,0.0002958961,0.001352603,0.0005845095,0.0003455777,0.0000354136,0.00005924509],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9790116,"threshold_uncertainty_score":0.751309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05652419110916653,"score_gpt":0.4034279627125305,"score_spread":0.3469037716033639,"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."}}