{"id":"W1974873233","doi":"10.1142/s0219720013410059","title":"ENHANCING GENOMICS INFORMATION RETRIEVAL THROUGH DIMENSIONAL ANALYSIS","year":2013,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Université de Neuchâtel; University of Melbourne","keywords":"Computer science; Linear subspace; Information retrieval; Dimension (graph theory); Rank (graph theory); Homogeneity (statistics); Graph; Data mining; Set (abstract data type); Genomics; Theoretical computer science; Machine learning; Mathematics; Genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001465085,0.001027125,0.00167743,0.005694883,0.0008050304,0.00206093,0.001009583,0.0007495236,0.0009966703],"category_scores_gemma":[0.004782307,0.0003229666,0.001277133,0.005916039,0.0005030039,0.003228199,0.001993613,0.0008224032,0.0008026701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006448193,"about_ca_system_score_gemma":0.0009876399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242022,"about_ca_topic_score_gemma":0.002480766,"domain_scores_codex":[0.9979005,0.0007080584,0.0001962002,0.0003043837,0.0007527631,0.0001381535],"domain_scores_gemma":[0.9981983,0.0005762927,0.0002375877,0.0003441585,0.0005867651,0.0000568046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002995736,0.0003362484,0.005722317,0.0004293605,0.0002806617,0.0001714487,0.0004913805,0.04948611,0.04260005,0.01987042,0.008312983,0.8719995],"study_design_scores_gemma":[0.00005235797,0.0002379327,0.0039538,0.00003961897,0.0002138114,0.0004092433,0.0003455644,0.9125256,0.02696209,0.04304286,0.01203854,0.0001785667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03266321,0.001573845,0.9617361,0.0004007382,0.00005896335,0.0001104033,0.0003673049,0.001574078,0.001515271],"genre_scores_gemma":[0.3170152,0.001466452,0.6775512,0.0002320919,0.0002379384,0.0002689604,0.001636758,0.0001193495,0.001472145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005694883,"threshold_uncertainty_score":0.007748187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00768950690829641,"score_gpt":0.2355901914549949,"score_spread":0.2279006845466985,"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."}}