{"id":"W3042874372","doi":"10.1093/bioinformatics/btaa456","title":"Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Institut de Valorisation des Données","keywords":"In silico; Computer science; RNA; Source code; Graph; Sequence (biology); Computational biology; RNase P; Artificial intelligence; Machine learning; Representation (politics); Exploit; Theoretical computer science; Biology; Genetics; Gene; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009335925,0.0001023023,0.0001172434,0.00003262236,0.0001016778,0.00002345136,0.0001333596,0.00006921797,0.00004726217],"category_scores_gemma":[0.0003284354,0.00009632231,0.0001111837,0.00005959713,0.0000332853,0.00001654551,0.00006449177,0.0000943272,0.000001938205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003565867,"about_ca_system_score_gemma":0.00004426703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000405267,"about_ca_topic_score_gemma":0.00000101633,"domain_scores_codex":[0.9992865,0.00002679141,0.0003307666,0.0001128789,0.0001132237,0.000129878],"domain_scores_gemma":[0.9994723,0.00002890802,0.0002331456,0.0001185922,0.000082798,0.00006427125],"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":[0.0001213585,0.000008017409,0.0001961079,0.0001737886,0.00006562284,2.549932e-7,0.0004861893,0.0008763908,0.9771588,0.000535927,0.0005831603,0.01979439],"study_design_scores_gemma":[0.0004029832,0.0003290891,0.0002002797,0.00001881704,0.00001897804,0.000005577602,0.0008016287,0.01357904,0.9793183,0.0005730126,0.004617278,0.0001350292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8309736,0.0001448272,0.1654249,0.0003803633,0.0001939478,0.0005535407,0.0001167325,0.00003242926,0.002179703],"genre_scores_gemma":[0.9697587,0.000009433567,0.02950817,0.0001943716,0.0002006539,0.00003208137,0.0001720183,0.00001357251,0.0001109229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1387852,"threshold_uncertainty_score":0.392791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989641482810419,"score_gpt":0.2637868994275682,"score_spread":0.243890484599464,"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."}}