{"id":"W2135667114","doi":"10.1016/j.febslet.2010.07.038","title":"From phenotype to gene: Detecting disease‐specific gene functional modules via a text‐based human disease phenotype network construction","year":2010,"lang":"en","type":"article","venue":"FEBS Letters","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Phenotype; Gene; Disease; Biology; Clinical phenotype; Genetics; Computational biology; Gene regulatory network; Function (biology); Gene expression; Medicine; Pathology","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":[0.0001295111,0.0002698477,0.0001668614,0.00005145048,0.0003495415,0.00009356788,0.00022586,0.0001323216,0.0001412197],"category_scores_gemma":[0.00002069024,0.0002807832,0.0001446963,0.0001212153,0.0001166999,0.00001001603,0.000113652,0.0002437799,0.00006490822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003001571,"about_ca_system_score_gemma":0.00005269383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003225329,"about_ca_topic_score_gemma":0.00005354392,"domain_scores_codex":[0.998538,0.00003852757,0.0003136882,0.0004907392,0.0001955502,0.0004235244],"domain_scores_gemma":[0.9987783,0.00001903682,0.0001356972,0.0006031833,0.00006526465,0.0003985077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004357643,0.00003736687,0.002961204,0.00001356859,0.00007562671,0.000007121007,0.0000272847,0.02201901,0.9546004,0.00009841243,0.01031649,0.009407766],"study_design_scores_gemma":[0.007619795,0.0005721349,0.5218832,0.00018808,0.0007216649,0.00002555497,0.0001270661,0.05798963,0.1298477,0.01052552,0.2648188,0.005680837],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8127516,0.0002075451,0.1844511,0.0007827764,0.001373216,0.0002488058,0.00009349437,0.00003880346,0.00005268095],"genre_scores_gemma":[0.9687649,0.000005521807,0.02044391,0.004265236,0.005225058,0.00004241851,0.001175736,0.00004739214,0.00002984431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8247527,"threshold_uncertainty_score":0.9999644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008063744721387427,"score_gpt":0.2010373423153131,"score_spread":0.1929735975939257,"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."}}