{"id":"W2009965219","doi":"10.1210/me.2014-1006","title":"Research Resource: EPSLiM: Ensemble Predictor for Short Linear Motifs in Nuclear Hormone Receptors","year":2014,"lang":"en","type":"article","venue":"Molecular Endocrinology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Anschutz Medical Campus, University of Colorado; Evans Medical Foundation; National Institute on Aging; Brigham and Women's Hospital; National Science Foundation","keywords":"Computational biology; Biology; Nuclear receptor; Androgen receptor; Transcription factor; Bioinformatics; Gene; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001383575,0.001539851,0.001294718,0.001331697,0.0003747852,0.000799651,0.002553238,0.0010794,0.02431778],"category_scores_gemma":[0.005918964,0.0005503197,0.001054396,0.002072152,0.0002258326,0.0008813009,0.001057865,0.001417629,0.01098625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004174351,"about_ca_system_score_gemma":0.001417084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00918837,"about_ca_topic_score_gemma":0.01145639,"domain_scores_codex":[0.9993344,0.0002015629,0.00004597355,0.0001423262,0.000223306,0.0000524479],"domain_scores_gemma":[0.9983996,0.0008950357,0.00008064539,0.0001698396,0.0003274337,0.0001274617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001578056,0.0004099245,0.009695961,0.00157643,0.0005269486,0.0004601338,0.00008033678,0.1901051,0.006981291,0.00280066,0.6327689,0.1530164],"study_design_scores_gemma":[0.0005836713,0.000263876,0.00256992,0.0001066493,0.00009700831,0.0001357357,0.00003267712,0.9327812,0.007273629,0.005362136,0.05072428,0.00006931073],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.1117311,0.01036846,0.3716595,0.003594064,0.001610516,0.000793522,0.350529,0.1327313,0.01698266],"genre_scores_gemma":[0.1926876,0.002957803,0.2155847,0.0007990787,0.0003483819,0.001233538,0.5649248,0.007126833,0.01433726],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02431778,"threshold_uncertainty_score":0.08135104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02332814584090992,"score_gpt":0.294331431801074,"score_spread":0.2710032859601641,"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."}}