{"id":"W4404801525","doi":"10.1016/j.procs.2024.09.616","title":"A biomarker identification model from protein protein interaction network using natural language processing and graph convolutional network","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Graph; Identification (biology); Artificial intelligence; Natural language processing; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004230295,0.0001430933,0.00009251149,0.0000579218,0.0002629404,0.0004337867,0.0002095623,0.000076251,0.000001355815],"category_scores_gemma":[0.00001134324,0.0001295666,0.0000362419,0.0003733319,0.0002099839,0.00007408752,0.0002234907,0.0001368901,0.000002426232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003113373,"about_ca_system_score_gemma":0.0002377952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009583994,"about_ca_topic_score_gemma":0.000008279446,"domain_scores_codex":[0.998796,0.00001597683,0.000239574,0.0004551359,0.0001858005,0.0003075456],"domain_scores_gemma":[0.9995692,0.000006267805,0.00009510103,0.0001559065,0.0001000788,0.00007338999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007588105,0.00002977463,0.0001939615,0.0002046209,0.00004556227,0.000003872135,0.0007921407,0.05091012,0.7363769,0.0007592461,0.0009819093,0.209626],"study_design_scores_gemma":[0.00009000221,0.00002038932,0.0003970101,0.0002061762,0.000009209402,0.00001857459,0.00001518102,0.994181,0.002840848,0.001889817,0.0001597781,0.0001719895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4904079,0.003709122,0.5051371,0.00005371874,0.00034784,0.0002868963,0.000004748365,0.0000299519,0.0000227111],"genre_scores_gemma":[0.9321954,0.000008631609,0.06678518,0.00009770686,0.0007752882,0.00003036129,0.000047307,0.00001157653,0.00004860672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9432709,"threshold_uncertainty_score":0.5283571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135384413387943,"score_gpt":0.2558028150679933,"score_spread":0.2444489709341139,"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."}}