{"id":"W2462225007","doi":"10.1073/pnas.1520964113","title":"Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; BC Cancer Agency; Canada's Michael Smith Genome Sciences Centre; Centre Hospitalier de l’Université de Montréal; Institute for Research in Immunology and Cancer; University of British Columbia","funders":"","keywords":"Multiple displacement amplification; Single cell sequencing; Single-cell analysis; Computational biology; Genome; Biology; Copy-number variation; Robustness (evolution); Single-nucleotide polymorphism; Copy number analysis; Genetics; Computer science; Cell; Gene; Exome sequencing; Polymerase chain reaction; Phenotype","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003503021,0.0004455258,0.0005267281,0.0003343885,0.0001473926,0.0006216479,0.0006326745,0.0005465708,0.0008268853],"category_scores_gemma":[0.00066845,0.000346988,0.0003705015,0.0003168532,0.0003097057,0.0004924771,0.0007014739,0.0006561777,0.0009232707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005649535,"about_ca_system_score_gemma":0.0003138958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054381,"about_ca_topic_score_gemma":0.001683808,"domain_scores_codex":[0.9994338,0.00004204995,0.00002503779,0.0001673639,0.0002837474,0.00004788416],"domain_scores_gemma":[0.9996728,0.0001415065,0.00006355585,0.00004486744,0.00005315316,0.00002421077],"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":[0.00001352013,0.000007331282,0.00021666,0.00004881208,0.000005659269,0.0000262504,0.00001823797,0.000865736,0.9927806,0.0002174014,0.0001408368,0.005658921],"study_design_scores_gemma":[0.000009476736,0.00008752582,0.001653404,0.000005525975,0.000008237222,0.0001284732,0.00003560942,0.02573815,0.9675328,0.0003045541,0.004472036,0.00002432501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5029778,0.003049096,0.478964,0.0006505277,0.0001559859,0.0004113526,0.002483535,0.004787982,0.006519676],"genre_scores_gemma":[0.6706505,0.001578169,0.3217014,0.0002615924,0.00003403605,0.0003766321,0.001996511,0.0002083345,0.003192642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001054381,"threshold_uncertainty_score":0.004099011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03433534732805769,"score_gpt":0.2447340668943058,"score_spread":0.2103987195662481,"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."}}