{"id":"W3035720628","doi":"10.1101/2020.06.11.146100","title":"Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Ontario Institute for Cancer Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Universität Bielefeld; Deutsche Forschungsgemeinschaft; Genome Canada; Mitacs; Eidgenössische Technische Hochschule Zürich; Canadian Institute for Advanced Research","keywords":"clone (Java method); Pairwise comparison; Tree (set theory); Biology; Computational biology; Computer science; Mathematics; Genetics; Combinatorics; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002508024,0.0008115696,0.001373054,0.001190184,0.0008114772,0.001605922,0.002160734,0.001346588,0.004176862],"category_scores_gemma":[0.01448032,0.0007466556,0.001661876,0.00131406,0.001090535,0.002789921,0.002334826,0.001876178,0.0007458091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546085,"about_ca_system_score_gemma":0.002061197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0067778,"about_ca_topic_score_gemma":0.01245137,"domain_scores_codex":[0.9987979,0.0003864506,0.00007813752,0.0003357339,0.0002703,0.0001314691],"domain_scores_gemma":[0.9891425,0.008118736,0.0004750804,0.001368722,0.0006258706,0.0002690173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000470187,0.0001031289,0.008707829,0.0002602063,0.0001078935,0.0002374309,0.0004301037,0.8076762,0.004779598,0.02759264,0.004930967,0.1447039],"study_design_scores_gemma":[0.00003409111,0.00002834179,0.0003624496,0.000011647,0.00001116704,0.00006385113,0.00004159741,0.9761537,0.001034808,0.02131519,0.0009362015,0.00000693064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1358436,0.0009634907,0.8542861,0.0007855725,0.00004214174,0.0001730606,0.001221288,0.004339976,0.002344945],"genre_scores_gemma":[0.374911,0.0002222894,0.6183181,0.0002614821,0.0000406622,0.0001785378,0.004104169,0.0005020116,0.001461684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0067778,"threshold_uncertainty_score":0.013973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006852475160566823,"score_gpt":0.1869354436325861,"score_spread":0.1800829684720192,"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."}}