{"id":"W4388036745","doi":"10.1016/j.csbj.2023.10.053","title":"GENI: A web server to identify gene set enrichments in tumor samples","year":2023,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; Israel Science Foundation; Hebrew University of Jerusalem; Israel Cancer Research Fund","keywords":"Set (abstract data type); Computational biology; Web server; Gene; Computer science; Biology; World Wide Web; The Internet; 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":[0.002226589,0.002449232,0.001761802,0.006405794,0.0006230458,0.001793184,0.001572883,0.0007107591,0.04224612],"category_scores_gemma":[0.00390331,0.0009888983,0.001606815,0.004613405,0.000436262,0.00106318,0.00233782,0.001264679,0.02128392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101898,"about_ca_system_score_gemma":0.001893048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002295827,"about_ca_topic_score_gemma":0.004880823,"domain_scores_codex":[0.9992808,0.0001196962,0.00006813449,0.0002268005,0.0002218911,0.0000827463],"domain_scores_gemma":[0.9978819,0.0009705171,0.0002996858,0.0003324053,0.0002948502,0.0002205062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00282688,0.0003365549,0.04007062,0.003437176,0.0009896187,0.000975009,0.0006642794,0.004379252,0.03106748,0.004291556,0.7406494,0.1703121],"study_design_scores_gemma":[0.001805962,0.0006600458,0.1317782,0.0006724828,0.0007391127,0.003676246,0.0005875722,0.07153171,0.05713731,0.0269053,0.703955,0.0005510794],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.02052378,0.002081469,0.07028512,0.0005090363,0.0003360799,0.0003799803,0.5263005,0.3693839,0.01020007],"genre_scores_gemma":[0.06121849,0.001588193,0.154778,0.001039384,0.0002713293,0.001675255,0.7394427,0.03286592,0.007120658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04224612,"threshold_uncertainty_score":0.1413274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534594088890696,"score_gpt":0.2917167153313813,"score_spread":0.2763707744424743,"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."}}