{"id":"W4283644199","doi":"10.1039/d1sc05681f","title":"GlyNet: a multi-task neural network for predicting protein–glycan interactions","year":2022,"lang":"en","type":"article","venue":"Chemical Science","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Glycomics Network; Alberta Innovates; Canada Foundation for Innovation; Compute Canada","keywords":"Glycan; Task (project management); Artificial neural network; Computer science; Artificial intelligence; Computational biology; Chemistry; Biology; Engineering; Biochemistry; Systems engineering; Glycoprotein","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":[0.0009836105,0.001934265,0.0008045385,0.0008437865,0.0003834104,0.0007808808,0.001425932,0.001522654,0.002291257],"category_scores_gemma":[0.002348042,0.00040985,0.000855791,0.0007267081,0.0003665366,0.001270523,0.0008602394,0.001482418,0.0008541605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249791,"about_ca_system_score_gemma":0.0009084183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01252356,"about_ca_topic_score_gemma":0.01251455,"domain_scores_codex":[0.9997596,0.0000660417,0.00001258887,0.00007848765,0.00004635823,0.0000368125],"domain_scores_gemma":[0.9994112,0.0003535853,0.00005210632,0.00003843343,0.0001005321,0.00004413999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006049874,0.0003190517,0.005171595,0.0001914739,0.0002701291,0.000183194,0.00004381348,0.8124249,0.003730041,0.002162679,0.01448952,0.1604087],"study_design_scores_gemma":[0.000008366994,0.00002763378,0.000186228,0.000005110457,0.000007161175,0.00001012112,0.000003850233,0.997685,0.0005916481,0.001148449,0.0003223525,0.000004074684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2903918,0.005122299,0.6718842,0.002149357,0.0006698836,0.0003429441,0.005753921,0.01545905,0.008226469],"genre_scores_gemma":[0.8111302,0.001275475,0.1701924,0.001088347,0.0001955419,0.000372788,0.007787462,0.0003212588,0.007636521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01252356,"threshold_uncertainty_score":0.02490133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377617892430973,"score_gpt":0.3161801546644267,"score_spread":0.292403975740117,"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."}}