{"id":"W2341203767","doi":"10.1093/bib/bbv075","title":"Collaborative science in the next-generation sequencing era: a viewpoint on how to combine exome sequencing data across sites to identify novel disease susceptibility genes","year":2015,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; National Institutes of Health; Memorial Sloan-Kettering Cancer Center; NIH Office of the Director; U.S. Department of Defense","keywords":"Exome sequencing; DNA sequencing; Exome; Computational biology; Biology; Genetics; Data science; Gene; Computer science; Mutation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001786859,0.0002157657,0.0001901158,0.0001260629,0.0002079477,0.000538773,0.0009221549,0.00006642857,0.000001050614],"category_scores_gemma":[0.001899498,0.0001734388,0.00003335238,0.0008243801,0.000156585,0.0001001709,0.000705689,0.000116042,0.000009512184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002739685,"about_ca_system_score_gemma":0.001010931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001708863,"about_ca_topic_score_gemma":0.001206302,"domain_scores_codex":[0.9982139,0.00005109459,0.0004251663,0.000460289,0.0004171467,0.0004324382],"domain_scores_gemma":[0.9982271,0.00002984449,0.0001380751,0.0009913869,0.0003287088,0.0002848612],"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":[0.0002271698,0.0002306004,0.003837611,0.0001369332,0.00001772033,0.00002907519,0.01593203,0.01468814,0.9561073,0.0003433102,0.004861264,0.003588832],"study_design_scores_gemma":[0.009630455,0.003381492,0.1528746,0.001178381,0.0001545026,0.0001896665,0.1405889,0.3952972,0.2513698,0.000950962,0.03911569,0.005268233],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935092,0.0002134394,0.002052812,0.002843415,0.0001314784,0.0007420445,0.0004503848,0.000009655249,0.00004758138],"genre_scores_gemma":[0.9854255,0.00005341502,0.007423989,0.006408629,0.00009909249,0.00004221611,0.0005193456,0.00001320852,0.0000146477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7047375,"threshold_uncertainty_score":0.7072631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1480056834594095,"score_gpt":0.355408906864513,"score_spread":0.2074032234051035,"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."}}