{"id":"W3121027314","doi":"10.1109/tcbb.2021.3050102","title":"DPCMNE: Detecting Protein Complexes From Protein-Protein Interaction Networks Via Multi-Level Network Embedding","year":2021,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Embedding; Similarity (geometry); Pairwise comparison; Cluster analysis; Granularity; Data mining; Theoretical computer science; Computational biology; Topology (electrical circuits); Artificial intelligence; Pattern recognition (psychology); Mathematics; Biology; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002727453,0.000363234,0.0003254572,0.00008265034,0.0006229789,0.0001100903,0.0002305527,0.0004568339,0.00004872044],"category_scores_gemma":[0.00004206907,0.0003529455,0.0001566743,0.000211212,0.0001665279,0.00004044814,0.00005021562,0.0005111985,0.00002260971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004016848,"about_ca_system_score_gemma":0.0001095492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002850824,"about_ca_topic_score_gemma":0.000118716,"domain_scores_codex":[0.9981394,0.000129744,0.0007575419,0.0003805611,0.0001401599,0.0004525393],"domain_scores_gemma":[0.9988194,0.0001123974,0.0003289497,0.0003761211,0.0002191763,0.000143953],"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":[0.0004905288,0.0003210502,0.0001332042,0.0001501766,0.0007488918,0.000008315675,0.000366151,0.671544,0.07849665,0.0004030864,0.0002162865,0.2471217],"study_design_scores_gemma":[0.001671644,0.0004690478,0.0003422563,0.0002675588,0.0000619,0.00009258478,0.0003758816,0.9556352,0.03366709,0.005189126,0.00149685,0.0007308102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1684402,0.0002370814,0.8299951,0.0001677871,0.0003919588,0.0005092147,0.000152701,0.00004476344,0.00006118035],"genre_scores_gemma":[0.7249227,0.00003308881,0.2733276,0.0004301577,0.000302141,0.00007341457,0.0007582522,0.00002354356,0.0001290342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5566674,"threshold_uncertainty_score":0.9998922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02445106034661882,"score_gpt":0.2799289310973304,"score_spread":0.2554778707507116,"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."}}