{"id":"W2146003645","doi":"10.1186/1471-2105-5-75","title":"Interaction profile-based protein classification of death domain","year":2004,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"In silico; Protein–protein interaction; Computational biology; Computer science; Protein structure prediction; Protein structure; Homology modeling; Protein function prediction; Structural genomics; Protein domain; Protein family; Docking (animal); Macromolecular docking; Protein sequencing; Artificial intelligence; Bioinformatics; Biology; Peptide sequence; Genetics; Protein function; Biochemistry; 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":[0.0005829745,0.0002987418,0.0003092371,0.002165531,0.0001708301,0.0004399368,0.0003144749,0.0003054998,0.001353525],"category_scores_gemma":[0.001373884,0.0000414732,0.0002988794,0.0008771673,0.0001283417,0.0002600736,0.000302374,0.0002700903,0.0006628294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002910286,"about_ca_system_score_gemma":0.0003573188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00093699,"about_ca_topic_score_gemma":0.0009864316,"domain_scores_codex":[0.9997091,0.00004823288,0.00003437083,0.00008121408,0.00007950696,0.00004762333],"domain_scores_gemma":[0.9990338,0.0003419911,0.0001740822,0.00006462337,0.0002823242,0.0001031647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001439724,0.0004015256,0.6475607,0.0004496226,0.0001812263,0.0003110019,0.0001201546,0.01612824,0.04663815,0.0006539519,0.004650129,0.2814656],"study_design_scores_gemma":[0.00007216787,0.0005242507,0.425031,0.00005511525,0.0001323683,0.00146765,0.0002284475,0.5326266,0.03262959,0.001932737,0.005261276,0.00003879229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709501,0.0004709129,0.02185634,0.00007496732,0.00001963266,0.0001216753,0.004284175,0.000633847,0.001588244],"genre_scores_gemma":[0.9747209,0.00008229714,0.01945763,0.00001862344,0.000009208798,0.00005623932,0.005312751,0.00001625895,0.0003261039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002165531,"threshold_uncertainty_score":0.004528046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893364968808356,"score_gpt":0.2507077440627499,"score_spread":0.2317740943746664,"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."}}