{"id":"W4402511634","doi":"10.1016/j.rineng.2024.102878","title":"DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model","year":2024,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quorum sensing; Artificial neural network; Identification (biology); Computer science; Computational biology; Artificial intelligence; Biological system; Biology; Ecology; Bacteria; Biofilm","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":[0.0002673771,0.00009966123,0.00009334087,0.00004353757,0.00001661346,0.00002699278,0.00009077402,0.00008276886,3.232671e-7],"category_scores_gemma":[0.00017743,0.0001023067,0.00004476518,0.0001457341,0.00001549114,0.000009759396,0.00006007418,0.0001364133,0.000002164142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001530822,"about_ca_system_score_gemma":0.00001790668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007901572,"about_ca_topic_score_gemma":0.000005946943,"domain_scores_codex":[0.9991944,0.00001156424,0.0003706623,0.0001547435,0.00008802512,0.0001806144],"domain_scores_gemma":[0.9996742,0.00002466439,0.00005061373,0.0002105952,0.00002195288,0.00001796266],"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":[0.00001192462,0.000002762612,0.00003459766,0.0001234474,0.00001109482,0.000001472911,0.0002345337,0.8622532,0.1356745,0.0002115843,0.0002121795,0.001228694],"study_design_scores_gemma":[0.0001124093,0.00001821686,0.0001581039,0.000104395,0.000005433926,0.000008629072,0.00002045086,0.965766,0.03261255,0.00004947118,0.00104327,0.0001010932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6922132,0.001301924,0.3042622,0.0001323879,0.0005009645,0.0001471408,0.00002181013,0.00008456675,0.00133577],"genre_scores_gemma":[0.9784682,0.00007685019,0.02107646,0.00001530088,0.0001446429,0.000001601732,0.00006474889,0.00002059708,0.0001315362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.286255,"threshold_uncertainty_score":0.4171947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007623267011142931,"score_gpt":0.248554836956474,"score_spread":0.240931569945331,"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."}}