{"id":"W4309242540","doi":"10.1109/biocas54905.2022.9948611","title":"Prediction of Protein-Protein Interactions through Deep Learning Based on Sequence Feature Extraction and Interaction Network","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Education; Ministry of Science and Technology","keywords":"Computer science; Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Process (computing); Feature extraction; Cross-validation; Field (mathematics); Protein function prediction; Set (abstract data type); Sequence (biology); Test set; Pattern recognition (psychology); Data mining; Protein function; Mathematics","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":[0.0004398687,0.001094238,0.0011169,0.001380616,0.000348517,0.0004991407,0.0007224597,0.0007939954,0.001055424],"category_scores_gemma":[0.0008568089,0.0004151547,0.0008720256,0.00127674,0.0002884678,0.001099702,0.0007429149,0.001046693,0.0004344571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006958536,"about_ca_system_score_gemma":0.0008868711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007024596,"about_ca_topic_score_gemma":0.007311377,"domain_scores_codex":[0.9996992,0.00004167707,0.00002028004,0.00009761034,0.00007742069,0.00006375209],"domain_scores_gemma":[0.9996707,0.0001319258,0.00006553199,0.000028317,0.00007318197,0.00003030447],"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":[0.0005469082,0.0007768925,0.02159955,0.0002607442,0.0003054515,0.0007079814,0.00007582269,0.5990547,0.03688167,0.004035792,0.008355021,0.3273996],"study_design_scores_gemma":[0.00000449757,0.00001323552,0.0008094137,0.000002019503,0.000007995054,0.00002123897,0.000002457176,0.9971586,0.001165211,0.000672925,0.0001390495,0.000003374299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3450063,0.002265376,0.6447815,0.000507506,0.0000706799,0.00009994039,0.001806198,0.00320191,0.002260659],"genre_scores_gemma":[0.8985654,0.0008498524,0.09333251,0.0001640461,0.00005259069,0.000134446,0.003742974,0.00006991442,0.003088328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007024596,"threshold_uncertainty_score":0.01396739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03705778356705998,"score_gpt":0.2796034131993648,"score_spread":0.2425456296323049,"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."}}