{"id":"W1995229312","doi":"10.1142/s0219720005001247","title":"SPIDER: SOFTWARE FOR PROTEIN IDENTIFICATION FROM SEQUENCE TAGS WITH <i>DE NOVO</i> SEQUENCING ERROR","year":2005,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software; Identification (biology); Sequence (biology); Computational biology; Computer science; Sequence assembly; Protein sequencing; DNA sequencing; Protein methods; Biology; Peptide sequence; Genetics; Programming language; Gene; Transcriptome","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":[0.002900815,0.001995193,0.001447852,0.002279755,0.0007535337,0.001548404,0.00265617,0.001042088,0.01923022],"category_scores_gemma":[0.00675602,0.001580492,0.001430242,0.001525218,0.0006465529,0.00222189,0.002681877,0.002187268,0.01674126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006302827,"about_ca_system_score_gemma":0.00135954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001200916,"about_ca_topic_score_gemma":0.001089791,"domain_scores_codex":[0.9986516,0.000218665,0.0001952082,0.0002713399,0.0005497871,0.0001135058],"domain_scores_gemma":[0.9975789,0.00107244,0.0003557751,0.0003556958,0.0005004627,0.0001367928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002797881,0.0002848749,0.01084121,0.003014018,0.000841692,0.001301213,0.0008144359,0.01516537,0.06970793,0.01475353,0.4001276,0.4803503],"study_design_scores_gemma":[0.0008759177,0.0004735568,0.0109154,0.0004932141,0.000308871,0.0044395,0.0002415948,0.3196637,0.2003271,0.04327009,0.418408,0.0005829489],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.006197682,0.0003746041,0.6031293,0.0001256484,0.0001481315,0.0002566404,0.006202926,0.3819161,0.001648957],"genre_scores_gemma":[0.05123097,0.0006986186,0.8288671,0.0004117251,0.00008549562,0.001288383,0.03760871,0.06891741,0.01089159],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01923022,"threshold_uncertainty_score":0.06433147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106295977740671,"score_gpt":0.2886165250035087,"score_spread":0.267553565226102,"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."}}