{"id":"W2803299750","doi":"10.1039/c8lc00184g","title":"Isolation and genome sequencing of individual circulating tumor cells using hydrogel encapsulation and laser capture microdissection","year":2018,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Laser capture microdissection; Encapsulation (networking); Isolation (microbiology); Microdissection; Genome; Nanotechnology; Computational biology; Chemistry; Biology; Molecular biology; Materials science; Genetics; Gene; Bioinformatics; Computer science; Gene expression","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":[0.0002429788,0.0002555828,0.000402261,0.000502478,0.0002063599,0.0003513329,0.0003276143,0.0004617542,0.0006120658],"category_scores_gemma":[0.0005306737,0.0001790147,0.0003600495,0.0004023671,0.0001875192,0.0001494731,0.0003827268,0.0004794334,0.0005346595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672794,"about_ca_system_score_gemma":0.0003443363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001066486,"about_ca_topic_score_gemma":0.002602322,"domain_scores_codex":[0.999667,0.00002289128,0.00002009139,0.0001401239,0.0001134317,0.00003641156],"domain_scores_gemma":[0.9997242,0.00009339093,0.00005112756,0.0000508663,0.00005091542,0.00002944317],"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":[0.00002555262,0.00001132798,0.0004859629,0.00002780538,0.000005930437,0.00005597847,0.00003909895,0.0003541712,0.991417,0.00009963294,0.0001133358,0.007364269],"study_design_scores_gemma":[0.00001677042,0.000129054,0.007824963,0.000009690398,0.00002539893,0.0003427917,0.00003392334,0.01350702,0.9730039,0.0002466205,0.004840874,0.00001897635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6147931,0.001807381,0.3727395,0.0003389784,0.00009467183,0.0004777597,0.005302147,0.001951827,0.002494636],"genre_scores_gemma":[0.5486246,0.001278655,0.4382985,0.0003930148,0.00003368692,0.0006895657,0.006627733,0.0004039342,0.003650374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001066486,"threshold_uncertainty_score":0.002120614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343431371377834,"score_gpt":0.2323146683824272,"score_spread":0.2188803546686488,"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."}}