{"id":"W2130614771","doi":"10.1093/bioinformatics/btt265","title":"DAPPLE: a pipeline for the homology-based prediction of phosphorylation sites","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phosphorylation; Pipeline (software); Computer science; Computational biology; Homology (biology); Model organism; Organism; Software; Sequence homology; Biology; World Wide Web; Bioinformatics; Programming language; Cell biology; Biochemistry; Genetics; Peptide sequence; Gene","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":[0.001083094,0.002007258,0.001122826,0.002212109,0.0008199126,0.001115777,0.002081063,0.0009278331,0.02421816],"category_scores_gemma":[0.002942157,0.001202529,0.00118034,0.001336652,0.0002908194,0.001340807,0.001468958,0.002055687,0.0146674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006095402,"about_ca_system_score_gemma":0.00110697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001387557,"about_ca_topic_score_gemma":0.002280501,"domain_scores_codex":[0.9996013,0.00005452044,0.00003926765,0.0001516732,0.0001117814,0.00004137519],"domain_scores_gemma":[0.9993467,0.0003025445,0.00006951817,0.00009694509,0.0001054606,0.00007878552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002983672,0.0003125667,0.007333388,0.004486626,0.0005425037,0.0009868463,0.0003729846,0.01881338,0.181987,0.00845487,0.5270959,0.2466303],"study_design_scores_gemma":[0.001489318,0.0005299086,0.02113562,0.0002968855,0.0003431618,0.00303344,0.0002273764,0.3743053,0.2180948,0.03987335,0.3402428,0.0004278757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02296264,0.001005665,0.5371435,0.0003873682,0.0002371632,0.000503463,0.1179165,0.3133683,0.006475304],"genre_scores_gemma":[0.1061217,0.0009541041,0.5735604,0.0004119486,0.00008979477,0.001552076,0.2953469,0.01590195,0.006061085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02421816,"threshold_uncertainty_score":0.08101785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162084757337295,"score_gpt":0.2390334012239839,"score_spread":0.2274125536506109,"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."}}