{"id":"W4233935614","doi":"10.1101/462812","title":"PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Recommender system; PSL; Computer science; Matrix decomposition; Machine learning; Subcellular localization; Artificial intelligence; Information retrieval; Mathematics; Biology; Gene","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":[0.001726949,0.001593176,0.001668114,0.002617671,0.0007946494,0.001174761,0.00243688,0.002347789,0.007345826],"category_scores_gemma":[0.005682781,0.000662711,0.001441217,0.002467583,0.0002237201,0.001688124,0.001155361,0.001551635,0.009076335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009021303,"about_ca_system_score_gemma":0.001408188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01980756,"about_ca_topic_score_gemma":0.0325758,"domain_scores_codex":[0.9986222,0.00036217,0.00008899513,0.0005193397,0.0003209569,0.00008638331],"domain_scores_gemma":[0.9977431,0.0008770154,0.0001505213,0.0005716184,0.0005443953,0.00011327],"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":[0.001201466,0.0006715292,0.02143898,0.002095849,0.001099045,0.0004805857,0.0002038218,0.09892012,0.01687606,0.003887065,0.4089966,0.4441288],"study_design_scores_gemma":[0.0001836118,0.0001882996,0.003290344,0.00007756537,0.0001499934,0.0003359215,0.00006040106,0.9563881,0.006330529,0.004454982,0.02843509,0.000105241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07285314,0.01043308,0.7144196,0.002869893,0.0008473595,0.0006345558,0.06580364,0.122849,0.009289741],"genre_scores_gemma":[0.2375122,0.002734709,0.651099,0.001197757,0.0003944934,0.0005663247,0.09306801,0.001420098,0.01200734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01980756,"threshold_uncertainty_score":0.03938454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328849395008648,"score_gpt":0.224955885027758,"score_spread":0.2116673910776715,"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."}}