{"id":"W2380654641","doi":"10.1073/pnas.1520693113","title":"Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasite <i>Leishmania</i>","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Research on Leishmaniasis Studies","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"Canadian Institutes of Health Research; Government of Canada","keywords":"Cosmid; Leishmania; Biology; Computational biology; Drug resistance; Leishmaniasis; Identification (biology); Function (biology); Drug discovery; Leishmania mexicana; Gene; Parasite hosting; Genetics; Bioinformatics; Computer science","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":[0.001237659,0.0006920861,0.000867725,0.0009299116,0.0009145784,0.001072902,0.0006882091,0.0005735139,0.004250056],"category_scores_gemma":[0.0009129081,0.000445826,0.0008625647,0.0006840788,0.0004598978,0.0005178138,0.000928036,0.001154581,0.002565881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000488992,"about_ca_system_score_gemma":0.0006624613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267319,"about_ca_topic_score_gemma":0.006246552,"domain_scores_codex":[0.9992437,0.000126654,0.00004808426,0.000225409,0.0003053806,0.0000507322],"domain_scores_gemma":[0.9993575,0.0002216983,0.0000861568,0.0001208104,0.0001355726,0.00007825738],"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":[0.0002001651,0.00007538898,0.002716886,0.0003389628,0.0001425327,0.0001056335,0.0001046521,0.0008675395,0.9638447,0.001095332,0.004456799,0.0260514],"study_design_scores_gemma":[0.0001085077,0.0003127788,0.02830935,0.00008143028,0.0002454397,0.0007567453,0.0002828002,0.04091794,0.8308596,0.003446666,0.09454955,0.0001290616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3058133,0.00491458,0.6199635,0.001915412,0.001037978,0.0009990466,0.03546477,0.01156814,0.01832323],"genre_scores_gemma":[0.3585881,0.00314502,0.5776767,0.002031593,0.0002756357,0.001169753,0.04474726,0.00189438,0.01047149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004250056,"threshold_uncertainty_score":0.01421785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0399738987222515,"score_gpt":0.3362389743823639,"score_spread":0.2962650756601124,"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."}}