{"id":"W2980515354","doi":"10.1101/797597","title":"An engineered CRISPR/Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Natural Sciences and Engineering Research Council of Canada; Leukemia and Lymphoma Society","keywords":"Biology; CRISPR; Stem cell; Computational biology; Context (archaeology); Transcriptome; Function (biology); Lineage (genetic); Gene; Genome editing; Single-cell analysis; Genetics; Cell; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002623097,0.000460041,0.0005654831,0.0001470281,0.0000494562,0.00005724864,0.0003487134,0.0007572205,0.000002747182],"category_scores_gemma":[0.0001685524,0.0004902123,0.0001156324,0.00009319693,0.00009888053,0.00001159624,0.0001579152,0.0002782512,8.018471e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007171698,"about_ca_system_score_gemma":0.0002205494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004460283,"about_ca_topic_score_gemma":0.000008636087,"domain_scores_codex":[0.9979791,0.00007478666,0.0005447771,0.000879955,0.0001596267,0.000361743],"domain_scores_gemma":[0.9981827,0.00005385076,0.0002939276,0.0009432118,0.0003774691,0.0001488351],"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":[0.0003019809,0.0003592454,0.0009141173,0.0007599848,0.00004024865,0.000009330529,0.00002904344,0.003478193,0.9940559,0.000006027982,0.0000422781,0.000003618622],"study_design_scores_gemma":[0.0008810792,0.0004811094,0.0001468102,0.0001580168,0.00004769201,2.115002e-8,0.000005044809,0.003223271,0.9929038,2.813134e-7,0.001619494,0.0005333229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782755,0.001810314,0.01740838,0.00001456258,0.0006716464,0.0009411415,0.0008229915,0.00005439607,0.000001108481],"genre_scores_gemma":[0.9766746,0.000337261,0.02229969,0.00003830853,0.0003940253,0.00008450282,0.0000124501,0.0001349956,0.00002420318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004891307,"threshold_uncertainty_score":0.999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271049021169588,"score_gpt":0.216022905898356,"score_spread":0.2033124156866602,"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."}}