{"id":"W3214070015","doi":"10.1038/s41467-021-26792-w","title":"Co-evolution based machine-learning for predicting functional interactions between human genes","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Israel Science Foundation; Hebrew University of Jerusalem; McGill University","keywords":"Phylogenetic tree; Annotation; Gene; Context (archaeology); Computational biology; Biology; Function (biology); Computer science; Machine learning; Artificial intelligence; Evolutionary biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689226,0.0007820433,0.0006900748,0.003041491,0.0006635929,0.0005917473,0.000661334,0.000803613,0.001896963],"category_scores_gemma":[0.004294618,0.0002456892,0.0009446915,0.002386845,0.000367711,0.0005743226,0.0007557746,0.001018598,0.0008701999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006734029,"about_ca_system_score_gemma":0.0006636926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00358904,"about_ca_topic_score_gemma":0.00496374,"domain_scores_codex":[0.9991755,0.0003391212,0.00004954916,0.0002510872,0.0001223895,0.00006222246],"domain_scores_gemma":[0.9974743,0.001907429,0.0001989578,0.0001661592,0.0001721958,0.0000810031],"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":[0.001244687,0.0005595194,0.1242939,0.000563729,0.001156713,0.0005998295,0.0002889015,0.4799655,0.02416726,0.005096665,0.008665288,0.3533979],"study_design_scores_gemma":[0.00001878435,0.0000462785,0.007986899,0.00001760317,0.00005289553,0.0001408774,0.00003289665,0.9826655,0.002089059,0.005688593,0.001247282,0.00001315899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4967771,0.004486436,0.4804337,0.0009938747,0.0001017651,0.0001806422,0.008046687,0.004412705,0.004567167],"genre_scores_gemma":[0.8419819,0.0004730843,0.1482456,0.0002103254,0.00007419622,0.0002035895,0.007451874,0.0001750193,0.001184271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00358904,"threshold_uncertainty_score":0.008933604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320815636047916,"score_gpt":0.3054068211603914,"score_spread":0.2821986647999122,"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."}}