{"id":"W4412796409","doi":"10.1101/2025.07.26.666948","title":"C3PI: Component Puzzle Protein-Protein Interaction Prediction","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Source code; Conditional random field; Code (set theory); Key (lock); Machine learning; Data mining; Web server; Artificial intelligence; Field (mathematics); Deep learning; Component (thermodynamics); The Internet","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":[0.0009238956,0.002972419,0.001396224,0.002444945,0.0007942383,0.001190238,0.003516509,0.002405966,0.008796566],"category_scores_gemma":[0.002818777,0.0007368701,0.00145915,0.002666824,0.0005839991,0.00166304,0.002279945,0.002232522,0.005906028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401072,"about_ca_system_score_gemma":0.002161971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01093333,"about_ca_topic_score_gemma":0.01203415,"domain_scores_codex":[0.9991581,0.00009793719,0.00003016018,0.000346127,0.0002678193,0.00009975355],"domain_scores_gemma":[0.9991384,0.0002251975,0.00009458784,0.0002764128,0.0001638239,0.0001016669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001546179,0.0005454523,0.0201461,0.001759145,0.0007285425,0.0006749178,0.0001035418,0.2264652,0.01960226,0.01237491,0.4664223,0.2496315],"study_design_scores_gemma":[0.00007811983,0.00007296646,0.002157713,0.00002678116,0.00004261677,0.0001845628,0.00001882778,0.9669171,0.00752437,0.00871503,0.01422537,0.00003645789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.222351,0.007790599,0.4077193,0.002101212,0.0007625607,0.0007604065,0.1191429,0.2204218,0.01895023],"genre_scores_gemma":[0.4191528,0.001227192,0.2951474,0.0008675655,0.0001478567,0.0006605858,0.2682151,0.003147832,0.01143368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01093333,"threshold_uncertainty_score":0.02942747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008425595960945978,"score_gpt":0.2305522638213413,"score_spread":0.2221266678603953,"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."}}