{"id":"W3173719549","doi":"10.1101/2021.06.21.449257","title":"A streamlined CRISPR workflow to introduce mutations and generate isogenic iPSCs for modeling amyotrophic lateral sclerosis","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pluripotent Stem Cells Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Office of Defense Programs; Québec Consortium for Drug Discovery; Canadian Institutes of Health Research; Canada First Research Excellence Fund; McGill University; U.S. Department of Defense","keywords":"CRISPR; Induced pluripotent stem cell; Genome editing; Amyotrophic lateral sclerosis; Biology; Mutation; Cas9; Genetics; Computational biology; Gene; Disease; Embryonic stem cell; Medicine","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":[0.0007845792,0.0006596969,0.0005140195,0.0007510337,0.0003729833,0.001020821,0.0008590025,0.0008644785,0.002194404],"category_scores_gemma":[0.0003735573,0.0004455908,0.0006490811,0.0003007448,0.0003537511,0.0002925868,0.0007009616,0.00164428,0.002062471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520096,"about_ca_system_score_gemma":0.0004604018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009275392,"about_ca_topic_score_gemma":0.0008662653,"domain_scores_codex":[0.9994406,0.00006088541,0.00008675574,0.0001305859,0.0002327091,0.00004849632],"domain_scores_gemma":[0.9997165,0.00004992395,0.00004764785,0.00007486729,0.00006425835,0.00004681931],"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":[0.00005477386,0.0000579806,0.0001994208,0.00009445474,0.00001925116,0.0002354594,0.00006637423,0.003326298,0.9838034,0.001666871,0.0007664154,0.009709386],"study_design_scores_gemma":[0.0000290124,0.0001638409,0.0005915224,0.0000281266,0.00003791242,0.0004426714,0.00003020149,0.01692273,0.9514346,0.0006788959,0.02959905,0.00004154963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1260305,0.0009643504,0.8468171,0.0002613494,0.0005245513,0.001306069,0.005900386,0.01006615,0.00812949],"genre_scores_gemma":[0.3960272,0.001771088,0.5788978,0.0002269901,0.00006148609,0.00264094,0.007241451,0.001521407,0.01161163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002194404,"threshold_uncertainty_score":0.007341027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0242713846049753,"score_gpt":0.2488249657977372,"score_spread":0.2245535811927619,"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."}}