{"id":"W2165980173","doi":"10.1186/s13071-014-0622-3","title":"Functional analysis of Girardia tigrina transcriptome seeds pipeline for anthelmintic target discovery","year":2015,"lang":"en","type":"article","venue":"Parasites & Vectors","topic":"Planarian Biology and Electrostimulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Allergy and Infectious Diseases; Fonds de recherche du Québec – Nature et technologies; Office of Biotechnology, Iowa State University; Iowa State University; National Institutes of Health; National Institute of Nursing Research; Compute Canada","keywords":"Planarian; Biology; Planaria; Druggability; Computational biology; RNA interference; Flatworm; Transcriptome; Caenorhabditis elegans; Genetics; Anthelmintic; Evolutionary biology; Gene; Zoology; RNA","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":[0.0004987053,0.001168119,0.0008618088,0.001450527,0.0008882742,0.001030114,0.000436593,0.0004275909,0.003526148],"category_scores_gemma":[0.0008267227,0.0003239481,0.001087475,0.00147778,0.0002161991,0.0004645369,0.0005946219,0.0007104723,0.002684194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005431866,"about_ca_system_score_gemma":0.001295089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044545,"about_ca_topic_score_gemma":0.003904501,"domain_scores_codex":[0.9996784,0.0000259219,0.00002226686,0.000126769,0.00009454388,0.00005220094],"domain_scores_gemma":[0.9996824,0.00006981343,0.00005243415,0.00003149109,0.000114933,0.00004899263],"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":[0.001020463,0.00009298594,0.01936104,0.00288854,0.0002278722,0.0006953726,0.0005888006,0.002173722,0.9062955,0.000639165,0.01033023,0.05568632],"study_design_scores_gemma":[0.0001705914,0.00164895,0.3501024,0.0007180928,0.001283501,0.002814827,0.001265477,0.03901904,0.4458228,0.00257066,0.154372,0.0002115494],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6900833,0.01278322,0.0525143,0.0009312978,0.0003286295,0.0005692393,0.2131988,0.0158969,0.01369435],"genre_scores_gemma":[0.4518837,0.005288368,0.1049396,0.0005118714,0.00008940345,0.0007147193,0.4284977,0.001774934,0.006299672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003526148,"threshold_uncertainty_score":0.01179618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207439557780792,"score_gpt":0.2680413458029486,"score_spread":0.2472973900248694,"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."}}