{"id":"W2048060724","doi":"10.1038/ncomms6871","title":"DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts","year":2014,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Terry Fox Research Institute; BC Cancer Agency","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Mitacs; Canada Research Chairs; Michael Smith Health Research BC; Canadian Breast Cancer Research Alliance","keywords":"Biology; clone (Java method); Breast cancer; Serial passage; Phenotype; Cancer research; In vivo; Somatic evolution in cancer; DNA; Human breast; Cell culture; Cell growth; Cancer; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000229773,0.0001154057,0.0001622964,0.00007160132,0.00009623541,0.00001893474,0.0007206926,0.0002247127,0.000005348947],"category_scores_gemma":[0.0003002504,0.0001252573,0.00006784147,0.0001491451,0.0001139451,0.000003749305,0.0004588187,0.0002700275,0.00000169862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002047082,"about_ca_system_score_gemma":0.00002714709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008601562,"about_ca_topic_score_gemma":0.002473413,"domain_scores_codex":[0.9991968,0.000130884,0.0002594245,0.000173362,0.00008683588,0.0001527552],"domain_scores_gemma":[0.9985768,0.00008559578,0.0001336243,0.0009676458,0.0001822579,0.00005407787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001852135,0.0001412415,0.02180372,0.00001668817,0.0000174251,3.748164e-7,0.00008223727,0.00001210697,0.9683917,0.007523972,0.001735681,0.0002563357],"study_design_scores_gemma":[0.002639175,0.0005065047,0.5524132,0.0001802262,0.0001125809,0.00002647456,0.0003687584,0.00009955661,0.4121595,0.002326619,0.0283967,0.0007707137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949384,0.001256234,0.0001027618,0.001092939,0.0001441046,0.0001761926,0.0001277318,0.00001020196,0.002151438],"genre_scores_gemma":[0.9967161,0.0008041365,0.001696882,0.0002217458,0.0001489468,0.00001513718,0.0003459875,0.00001818273,0.00003283424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5562322,"threshold_uncertainty_score":0.5107845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275623981029214,"score_gpt":0.2712311594502416,"score_spread":0.2584749196399495,"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."}}