{"id":"W2255915162","doi":"10.1101/027540","title":"Accelerating Gene Discovery by Phenotyping Whole-Genome Sequenced Multi-Mutation Strains and Using the Sequence Kernel Association Test (SKAT)","year":2015,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC; Universities Space Research Association; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Biology; Gene; Genetic screen; Genetics; Forward genetics; Phenotype; Computational biology; Mutation; Presenilin; Alzheimer's disease","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":[0.002387531,0.001203144,0.001119896,0.002347341,0.0004807933,0.0009548077,0.000842872,0.0007383424,0.004576466],"category_scores_gemma":[0.00252726,0.0004588643,0.00183332,0.001230109,0.0006082261,0.0004782879,0.001253105,0.001371726,0.001782016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002340539,"about_ca_system_score_gemma":0.0004598298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005707208,"about_ca_topic_score_gemma":0.001083826,"domain_scores_codex":[0.9987404,0.0003656785,0.0001661765,0.000395656,0.0002452683,0.00008683762],"domain_scores_gemma":[0.9956968,0.002866441,0.0006322249,0.0003510154,0.000221211,0.0002322888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008503922,0.0003494019,0.01791425,0.0006417298,0.0005132066,0.0008538748,0.0001041193,0.004437292,0.9185113,0.001853327,0.00116715,0.05280394],"study_design_scores_gemma":[0.0003304559,0.001838054,0.06140062,0.00008853479,0.0007116077,0.003673848,0.0001543612,0.1522785,0.7600458,0.005290384,0.01398645,0.0002013893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3749751,0.0003754926,0.6052059,0.0002729611,0.0001122063,0.0003814447,0.006404982,0.01053031,0.001741666],"genre_scores_gemma":[0.4849177,0.0004460555,0.502277,0.0002834951,0.00003168359,0.0007477808,0.007610891,0.001379434,0.002305892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004576466,"threshold_uncertainty_score":0.01530981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280208000361408,"score_gpt":0.2513788262847185,"score_spread":0.2185767462811045,"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."}}