{"id":"W4313594831","doi":"10.1016/j.bbapap.2023.140889","title":"Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores","year":2023,"lang":"en","type":"article","venue":"Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Genome British Columbia","funders":"National Key Research and Development Program of China; Chinese Academy of Sciences","keywords":"KEGG; Gene ontology; Computational biology; String (physics); Gene; Feature (linguistics); Biology; Node (physics); Human proteins; Computer science; Stability (learning theory); Machine learning; Genetics; Gene expression; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001034657,0.0002854618,0.0004255731,0.0003473034,0.0001802673,0.0001741468,0.0002494517,0.0001240966,0.000002417517],"category_scores_gemma":[0.0000997826,0.0002575516,0.0001104579,0.001140984,0.0001957249,0.0003665527,0.0003628172,0.0002415946,9.774097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005634894,"about_ca_system_score_gemma":0.0001340449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001237231,"about_ca_topic_score_gemma":0.00002223152,"domain_scores_codex":[0.997586,0.0005140748,0.000455219,0.000814122,0.0003312581,0.0002993927],"domain_scores_gemma":[0.9986307,0.0002710459,0.0003478635,0.0005151937,0.0001099837,0.0001251815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005972582,0.0004629449,0.001676959,0.0003277726,0.0003834738,0.000005083366,0.00095139,0.005643408,0.9696556,0.004207283,0.000018035,0.01607084],"study_design_scores_gemma":[0.0004879337,0.0006826128,0.1131338,0.00007579364,0.0000686174,0.000002447109,0.00001918316,0.4208702,0.4606201,0.003788035,0.00001863433,0.0002325689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130532,0.00002628622,0.0835453,0.001325997,0.00007808561,0.001713156,0.0001121791,0.0001140318,0.00003175395],"genre_scores_gemma":[0.939806,0.00005024829,0.05959033,0.00008535178,0.00006951601,0.0003092838,0.00006634548,0.00001475581,0.00000818739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5090355,"threshold_uncertainty_score":0.9999877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669606790462085,"score_gpt":0.2680735352151083,"score_spread":0.2413774673104875,"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."}}