{"id":"W1973262365","doi":"10.1016/j.jmb.2005.05.037","title":"Armadillo: Domain Boundary Prediction by Amino Acid Composition","year":2005,"lang":"en","type":"article","venue":"Journal of Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Linker; Armadillo; Computational biology; Protein domain; Domain (mathematical analysis); Amino acid; Peptide sequence; Sequence (biology); Biological system; Computer science; Biology; Genetics; Mathematics; Cell biology","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":[0.0006557248,0.001607598,0.001158127,0.001382878,0.0005676735,0.001090542,0.001480107,0.0008749069,0.01361735],"category_scores_gemma":[0.001406454,0.0006986973,0.001046795,0.0006516683,0.0002616724,0.0008612917,0.001272986,0.001156541,0.00555557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006304577,"about_ca_system_score_gemma":0.0006066655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00209623,"about_ca_topic_score_gemma":0.003398338,"domain_scores_codex":[0.9997653,0.00003567337,0.0000114766,0.00009484942,0.00006109694,0.0000315591],"domain_scores_gemma":[0.9996724,0.0001470073,0.00004547864,0.00005477229,0.0000266663,0.00005360615],"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":[0.01134206,0.0004929624,0.01069948,0.001380339,0.0005046683,0.0007889388,0.000145104,0.0158991,0.1650179,0.003710659,0.1879158,0.6021031],"study_design_scores_gemma":[0.002616173,0.0005969384,0.008390426,0.0001857218,0.0005490152,0.000841821,0.00008296296,0.6655889,0.1952787,0.01543955,0.1101906,0.0002391393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.1783993,0.002917197,0.3176735,0.0009101086,0.0005304888,0.0003791584,0.02147853,0.4686469,0.00906474],"genre_scores_gemma":[0.4086631,0.001233045,0.5120777,0.0007240626,0.0002418075,0.0008762127,0.04670839,0.01604115,0.01343447],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01361735,"threshold_uncertainty_score":0.04555452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002839061238249694,"score_gpt":0.2474695688552085,"score_spread":0.2446305076169588,"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."}}