{"id":"W3004238495","doi":"10.1038/s41598-019-56895-w","title":"PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Carleton University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Grain Farmers of Ontario","keywords":"Computational biology; Computer science; Interactome; Biology; Protein–protein interaction; Arabidopsis; Caenorhabditis elegans; Artificial intelligence; Machine learning; Bioinformatics; Genetics; Gene","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":[0.00165845,0.002338971,0.001097176,0.001991932,0.0008033178,0.001366098,0.001605559,0.001259945,0.01044926],"category_scores_gemma":[0.0049085,0.0009893615,0.001817218,0.001893571,0.0003736969,0.002068704,0.001911386,0.002158588,0.008067248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000615544,"about_ca_system_score_gemma":0.001516823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187326,"about_ca_topic_score_gemma":0.003869545,"domain_scores_codex":[0.9992995,0.0001238379,0.00004160344,0.0002516159,0.0002088809,0.00007461942],"domain_scores_gemma":[0.9989373,0.0004755987,0.00008833127,0.0001836684,0.0002445139,0.00007055896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0033698,0.0005273054,0.03482952,0.002733091,0.001362443,0.0009600307,0.0005512412,0.08922272,0.09739177,0.009316308,0.3125465,0.4471892],"study_design_scores_gemma":[0.0002187383,0.0003299305,0.01059257,0.00008349741,0.0001783678,0.0005570459,0.0001441406,0.8738522,0.04041142,0.01723539,0.05628781,0.0001089299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07059929,0.001255218,0.7525451,0.0005488093,0.0002732902,0.0003732005,0.03396735,0.1358738,0.004564033],"genre_scores_gemma":[0.2182654,0.0009309947,0.6171364,0.0003353805,0.0001149995,0.001256112,0.1448619,0.008340455,0.008758361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01044926,"threshold_uncertainty_score":0.03495628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02161912187887638,"score_gpt":0.2654608307286541,"score_spread":0.2438417088497777,"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."}}