{"id":"W2066123704","doi":"10.1142/s0219720006002119","title":"IMPROVING THE SENSITIVITY AND SPECIFICITY OF PROTEIN HOMOLOGY SEARCH BY INCORPORATING PREDICTED SECONDARY STRUCTURES","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homology (biology); Protein secondary structure; Protein structure prediction; Homology modeling; Computer science; Computational biology; Sensitivity (control systems); Protein structure; Machine learning; Artificial intelligence; Biology; Genetics; Amino acid; Engineering; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005745534,0.00009145817,0.0001728486,0.00004908363,0.00009372473,0.0000199078,0.00007082964,0.0001107214,0.000003086597],"category_scores_gemma":[0.00005207829,0.0000607455,0.0000385916,0.0000417285,0.0002289576,0.00001009177,0.00007987161,0.0001204729,9.17463e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004650181,"about_ca_system_score_gemma":0.00009037698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003081908,"about_ca_topic_score_gemma":0.000005497487,"domain_scores_codex":[0.9991651,0.0001319683,0.0004316015,0.00007652513,0.0000920661,0.0001027058],"domain_scores_gemma":[0.9991774,0.00008547617,0.0004584057,0.00006298169,0.0001832102,0.0000325117],"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":[0.0001145949,0.00002672098,0.001443406,0.00006591075,0.00006101466,0.000002060628,0.00006435176,0.0005072705,0.9562183,0.004977413,0.0001265538,0.03639242],"study_design_scores_gemma":[0.002309198,0.003337968,0.02537409,0.0000706393,0.00006760297,0.001626214,0.0008534168,0.03691721,0.8724142,0.05522069,0.001326465,0.0004822517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9276026,0.000674582,0.07126242,0.0001552242,0.00003956805,0.0001208543,0.00007297264,0.000001630238,0.00007012412],"genre_scores_gemma":[0.977634,0.00002453297,0.02212135,0.00006330026,0.00009278032,0.000001154785,0.00005139879,0.000004246751,0.000007276641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08380402,"threshold_uncertainty_score":0.247713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005744006339003689,"score_gpt":0.2114446031538455,"score_spread":0.2057005968148418,"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."}}