{"id":"W3000659041","doi":"10.1186/s12859-019-3312-5","title":"Optimization and expansion of non-negative matrix factorization","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada; Government of Ontario; Canadian Institutes of Health Research; Ontario Genomics; Genome Canada; Government of Canada; Ontario Institute for Cancer Research; Ontario Genomics Institute; Movember Foundation","keywords":"Non-negative matrix factorization; Computer science; Matrix (chemical analysis); Matrix decomposition; Computational biology; Biology; Mathematics; Physics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003053607,0.001408677,0.0009150195,0.0007842839,0.0004818881,0.0009956132,0.001023458,0.001357456,0.00435274],"category_scores_gemma":[0.01038909,0.0004777343,0.001251273,0.000864881,0.00138872,0.001337394,0.001280488,0.002053114,0.002549506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009779045,"about_ca_system_score_gemma":0.001759429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003192242,"about_ca_topic_score_gemma":0.0039182,"domain_scores_codex":[0.9986965,0.0005575569,0.00005635079,0.0002527035,0.0003504392,0.00008651086],"domain_scores_gemma":[0.9965805,0.001966431,0.0003471964,0.0003483297,0.0006274074,0.0001302288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001178491,0.00008812379,0.0009036289,0.0007316152,0.00008642121,0.0003057685,0.0002363733,0.6800789,0.008995838,0.130224,0.01778882,0.1604427],"study_design_scores_gemma":[0.00001054865,0.00002155662,0.0001586881,0.00003817861,0.00000655494,0.00007396939,0.0000174313,0.9549639,0.001183379,0.03922664,0.004287324,0.0000119024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001620131,0.0002423784,0.9967676,0.0002018796,0.00004368614,0.0000271091,0.00008807119,0.0001654077,0.0008437496],"genre_scores_gemma":[0.07034129,0.000768368,0.9242586,0.0002082012,0.0001687757,0.0002696975,0.0005364539,0.0003043729,0.003144186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00435274,"threshold_uncertainty_score":0.01614922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04404803521713629,"score_gpt":0.3093856997125112,"score_spread":0.2653376644953749,"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."}}