{"id":"W2100284033","doi":"10.1186/1471-2105-15-344","title":"CIG-P: Circular Interaction Graph for Proteomics","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity Western University; Western University","funders":"Trinity Western University","keywords":"Visualization; Proteomics; Computer science; AKA; Computational biology; Graph; Data mining; Biology; Biochemistry; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000129738,0.0001289097,0.0001323968,0.0000455759,0.0001315672,0.00004660805,0.0001896217,0.0001180941,0.00003463994],"category_scores_gemma":[0.00009010274,0.0001257411,0.0001083755,0.00007645681,0.00003426645,0.0001973949,0.0000402293,0.0001283862,0.00002668466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004679999,"about_ca_system_score_gemma":0.00002233623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002648907,"about_ca_topic_score_gemma":0.000001841094,"domain_scores_codex":[0.9992605,0.000003054225,0.0003469556,0.0001120592,0.00008809,0.0001893569],"domain_scores_gemma":[0.9992002,0.00006869218,0.0002084011,0.0003887095,0.00007551558,0.00005843969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004409863,0.0007696972,0.004498454,0.01277593,0.0002404137,5.035295e-7,0.002461912,0.01221916,0.3287664,0.439684,0.01439549,0.183747],"study_design_scores_gemma":[0.000659856,0.000046372,0.00001721839,0.0000804951,0.00003351937,0.00001460075,0.0002757591,0.6244168,0.2033141,0.04668906,0.1240475,0.0004046815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0081981,0.000006396705,0.9811743,0.00005317844,0.00003684475,0.0004296668,0.00003419243,0.0002354478,0.009831866],"genre_scores_gemma":[0.02746614,0.00001590214,0.9712891,0.0001273164,0.0001143249,0.0006248073,0.000128197,0.00002428741,0.0002099168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6121976,"threshold_uncertainty_score":0.5127573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974616961840828,"score_gpt":0.2766336423122025,"score_spread":0.2568874726937942,"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."}}