{"id":"W4285121953","doi":"10.1109/tnse.2022.3185717","title":"Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Training Program for Excellent Young Innovators of Changsha; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Centrality; Leverage (statistics); Computer science; Construct (python library); Network analysis; Identification (biology); Data mining; Biological network; Computational biology; Artificial intelligence; Biology; Computer network; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001094844,0.0003634655,0.0003644714,0.002846604,0.0004686833,0.0005772672,0.0004625705,0.0003691864,0.0005837924],"category_scores_gemma":[0.005472855,0.0001870437,0.0004695764,0.002363573,0.0006782855,0.001175391,0.0006956338,0.0004379621,0.00008218731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000978742,"about_ca_system_score_gemma":0.0006349988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00434003,"about_ca_topic_score_gemma":0.003921261,"domain_scores_codex":[0.9993976,0.0001295367,0.00004302883,0.0001709866,0.0001794159,0.00007936842],"domain_scores_gemma":[0.9969743,0.001599619,0.0006053841,0.0001923236,0.0004593903,0.000168888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005058573,0.0001101958,0.08392479,0.0005168281,0.0003125691,0.001078778,0.0004752516,0.6944488,0.06512602,0.06572641,0.001749682,0.08602475],"study_design_scores_gemma":[0.000008304384,0.00003683574,0.02031729,0.00001314706,0.00006567332,0.0003427631,0.0001059722,0.9549402,0.006040503,0.01681743,0.001289588,0.00002241341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5463002,0.001294246,0.448283,0.0002436676,0.0000250029,0.00005336674,0.0008423316,0.0003716554,0.002586624],"genre_scores_gemma":[0.9793714,0.0003513731,0.01940897,0.00001805409,0.00001548502,0.00002582002,0.0004609276,0.00002203395,0.0003258649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00434003,"threshold_uncertainty_score":0.008629501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005785514114959971,"score_gpt":0.2076366836842331,"score_spread":0.2018511695692731,"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."}}