{"id":"W4377965899","doi":"10.1101/2023.05.23.541863","title":"sncRNAP: Prediction and profiling of full sncRNA repertoires from sRNAseq data","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Science Foundation Ireland; EU Joint Programme – Neurodegenerative Disease Research","keywords":"Computational biology; Biology; Gene expression profiling; microRNA; Gene; Genetics; Gene expression","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":[0.002621509,0.0014604,0.0009361182,0.002228369,0.0008531506,0.001367252,0.001037283,0.0006930415,0.004449759],"category_scores_gemma":[0.004502447,0.000658186,0.001917535,0.001390873,0.0003680801,0.001090389,0.001185475,0.001205909,0.003587548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006611027,"about_ca_system_score_gemma":0.001491835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003795407,"about_ca_topic_score_gemma":0.007300314,"domain_scores_codex":[0.9985475,0.000134727,0.0001211145,0.0007087513,0.0003636893,0.0001242931],"domain_scores_gemma":[0.9985093,0.0006452472,0.0001908859,0.0001935806,0.0003457961,0.0001150859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004303428,0.0004466577,0.09480753,0.005284432,0.001712975,0.002007065,0.001642727,0.02601743,0.5027757,0.004377127,0.1358626,0.2207622],"study_design_scores_gemma":[0.0004614222,0.0009631707,0.1077903,0.0004459651,0.0006082333,0.0015811,0.0006197271,0.4391179,0.3163282,0.00815098,0.1234245,0.0005085538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.2848097,0.002210878,0.2713833,0.0005425903,0.0003154436,0.0009522138,0.3258953,0.1085107,0.005379923],"genre_scores_gemma":[0.2144286,0.0006516598,0.4296884,0.0006080785,0.00007946248,0.001368979,0.3434198,0.006980813,0.002774134],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004449759,"threshold_uncertainty_score":0.0148859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953324141089468,"score_gpt":0.2602059161915323,"score_spread":0.2306726747806376,"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."}}