{"id":"W2900724112","doi":"10.1101/474379","title":"Identification of a putative nuclear localization signal in maspin protein shed light into its nuclear import regulation","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Silk-based biomaterials and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universidade de São Paulo; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Maspin; Nuclear localization sequence; Subcellular localization; Nuclear export signal; Cell biology; Cell nucleus; Nuclear transport; Biology; Nuclear protein; Nucleus; NLS; HeLa; Gene; Cell; Biochemistry; Genetics; Cytoplasm; Transcription factor; Cancer","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":[0.0001172309,0.0002527657,0.0001845981,0.0001377917,0.0001991305,0.0002458531,0.0001963572,0.0003316487,0.001615318],"category_scores_gemma":[0.0001335492,0.0000815407,0.00041055,0.0001026147,0.0002285204,0.0002338638,0.0001736941,0.0005271738,0.0007641522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003346514,"about_ca_system_score_gemma":0.0001469306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003158013,"about_ca_topic_score_gemma":0.0003878154,"domain_scores_codex":[0.9999242,0.00001266638,0.000008401142,0.00002489198,0.00001872037,0.00001113838],"domain_scores_gemma":[0.9998518,0.00003261795,0.00003662675,0.00001696029,0.00002203369,0.00003998287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002962233,0.000004879959,0.0001066197,0.00002590922,0.000001942341,0.0001167637,0.000008043046,0.00003618289,0.9990466,0.0001726922,0.00002351627,0.0004272571],"study_design_scores_gemma":[0.00001220804,0.00007006133,0.004127064,0.00001209883,0.00001464196,0.0005095021,0.00003347426,0.002355841,0.9882758,0.0001774919,0.004405315,0.00000640387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711719,0.001271378,0.02485769,0.0003753823,0.00009336995,0.00003065779,0.0006360192,0.000161371,0.001402251],"genre_scores_gemma":[0.9796783,0.0005906519,0.01411664,0.0001686683,0.00003113165,0.000036391,0.00197117,0.00004903581,0.003357933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001615318,"threshold_uncertainty_score":0.005403757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044267151473857,"score_gpt":0.2272575602509546,"score_spread":0.216814888736216,"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."}}