{"id":"W2111607030","doi":"10.1186/1471-2105-10-202","title":"NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Nuclear Structure and Function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":719,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nuclear localization sequence; NLS; Hidden Markov model; Nuclear transport; Markov chain; Computational biology; Simple (philosophy); Computer science; Set (abstract data type); Markov model; Artificial intelligence; Biology; Machine learning; Genetics; Cell nucleus; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008165409,0.0001213351,0.00009190753,0.00003839135,0.0001296215,0.00004304642,0.00009594805,0.0001767636,0.00001942086],"category_scores_gemma":[0.000025401,0.0001146512,0.00008266368,0.00005727396,0.00002279919,0.00001854944,0.00002033716,0.00004816871,0.000008588839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001657183,"about_ca_system_score_gemma":0.00003800949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.882262e-7,"about_ca_topic_score_gemma":0.000003858069,"domain_scores_codex":[0.9993367,0.000008112066,0.0002495621,0.0001242989,0.0001076448,0.0001736449],"domain_scores_gemma":[0.999579,0.000003961866,0.00009477082,0.000193809,0.00007029054,0.00005815989],"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":[0.002598208,0.0003507094,0.001155102,0.0006926798,0.0002121502,7.429394e-7,0.002192993,0.1279342,0.06004418,0.006468222,0.3532727,0.4450782],"study_design_scores_gemma":[0.0005257229,0.0004170691,0.0003963802,0.000006337794,0.00002838573,0.000009579596,0.0001119004,0.9812803,0.0008518189,0.0007320359,0.01550229,0.0001381691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02341065,0.00002708068,0.9742236,0.00004301744,0.0000977616,0.0003253476,0.00008895742,0.00005539286,0.001728248],"genre_scores_gemma":[0.7820001,0.00004057433,0.2141764,0.001534693,0.0004325156,0.00000774197,0.001449674,0.00003497381,0.0003233043],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8533461,"threshold_uncertainty_score":0.467534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034064885787714,"score_gpt":0.2196102180540038,"score_spread":0.2092695691961267,"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."}}