{"id":"W2943637588","doi":"10.1101/622050","title":"TimeTeller: a New Tool for Precision Circadian Medicine and Cancer Prognosis","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Circadian rhythm and melatonin","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; University of Warwick; Directorate for Biological Sciences; Institut National de la Santé et de la Recherche Médicale; Cancer Research UK","keywords":"Circadian rhythm; Circadian clock; Breast cancer; Biomarker; Metric (unit); Cancer; Internal medicine; Precision medicine; Oncology; Medicine; Disease; Bioinformatics; Computational biology; Biology; Pathology; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005137139,0.0006398361,0.0008137651,0.000413289,0.0002025092,0.0002028991,0.000543434,0.0006379305,0.0001817002],"category_scores_gemma":[0.001328008,0.0006213879,0.0001708961,0.0004823341,0.0002031702,0.0002096309,0.0003876979,0.0006556729,0.00009540226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002252189,"about_ca_system_score_gemma":0.001046147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121918,"about_ca_topic_score_gemma":0.000002332319,"domain_scores_codex":[0.9962761,0.0001137518,0.000605059,0.001770839,0.000477353,0.0007568393],"domain_scores_gemma":[0.9972242,0.0004861454,0.0004365822,0.001103355,0.000203371,0.0005463617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001040369,0.00008127013,0.009345582,0.0008470803,0.00007157306,0.00002950534,0.00009449127,0.00005913149,0.976964,0.002807664,0.009367884,0.0002277941],"study_design_scores_gemma":[0.001903503,0.0001853196,0.03828871,0.001705833,0.000242141,9.554784e-8,0.000002071686,0.00127175,0.8037264,0.00001465474,0.1514637,0.001195869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974321,0.01091409,0.002252475,0.00254468,0.004074258,0.004855331,0.0005248251,0.00046676,0.00004655552],"genre_scores_gemma":[0.9914024,0.00308461,0.001864535,0.001124839,0.001465498,0.0006853503,2.584074e-7,0.0001957722,0.0001767667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1732376,"threshold_uncertainty_score":0.9996237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607111799214102,"score_gpt":0.2650357469693496,"score_spread":0.2289646289772086,"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."}}