{"id":"W2965510019","doi":"10.1186/s13015-019-0152-9","title":"A multi-labeled tree dissimilarity measure for comparing “clonal trees” of tumor progression","year":2019,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Institute of General Medical Sciences; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"clone (Java method); Tree (set theory); Measure (data warehouse); Vertex (graph theory); Mutation; Biology; Computational biology; Computer science; Granularity; Combinatorics; Mathematics; Genetics; Gene; Data mining; Graph","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":[0.001547832,0.0004506954,0.0008872912,0.004801199,0.0007152645,0.001457922,0.00127951,0.001075956,0.001315561],"category_scores_gemma":[0.008541027,0.0002340559,0.0008667624,0.003394957,0.0008910613,0.002934256,0.001367133,0.001042042,0.0002839343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125398,"about_ca_system_score_gemma":0.0006267262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170895,"about_ca_topic_score_gemma":0.001650244,"domain_scores_codex":[0.9983605,0.0003346872,0.0001604852,0.0004006056,0.0006195863,0.0001241692],"domain_scores_gemma":[0.9953912,0.001888096,0.0009125606,0.0005918719,0.0008466393,0.0003695416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006994897,0.0004016554,0.03932396,0.0006670174,0.0004334803,0.0004698194,0.0007122126,0.2781038,0.08116603,0.1502837,0.006574357,0.4411644],"study_design_scores_gemma":[0.00003214733,0.0002931346,0.01297402,0.00004682029,0.0000600266,0.000731241,0.0001677318,0.8778915,0.01067331,0.09007011,0.00697328,0.00008658177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1051405,0.0005760584,0.8914808,0.000145817,0.00007156905,0.00008020223,0.0006231469,0.0003612749,0.00152059],"genre_scores_gemma":[0.5353949,0.000227917,0.4612284,0.0001066257,0.0001357025,0.0001619372,0.001695989,0.0001648758,0.0008836673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004801199,"threshold_uncertainty_score":0.009098291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0228347557084764,"score_gpt":0.3166128142024556,"score_spread":0.2937780584939791,"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."}}