{"id":"W2789974803","doi":"10.1186/s13015-018-0123-6","title":"Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment","year":2018,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Computer science; Pairwise comparison; Cosine similarity; Artificial neural network; Similarity (geometry); Multiple sequence alignment; Artificial intelligence; Data mining; Smith–Waterman algorithm; Function (biology); Solver; Sequence alignment; Pattern recognition (psychology); Sequence (biology); Algorithm; Machine learning; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002900475,0.0002040736,0.000231275,0.00002577229,0.000313105,0.00001333662,0.0002885294,0.0001290756,0.000001700596],"category_scores_gemma":[0.0001490903,0.0001488117,0.000152228,0.0001173771,0.0003459024,0.000001030599,0.0001777291,0.0000526951,2.926232e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001908543,"about_ca_system_score_gemma":0.00004958017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160273,"about_ca_topic_score_gemma":0.00007005491,"domain_scores_codex":[0.9987766,0.00006577962,0.0002461607,0.0003967013,0.00006149655,0.0004532957],"domain_scores_gemma":[0.999012,0.00007676947,0.000193008,0.0003975333,0.000279303,0.00004140211],"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":[0.0004319809,0.0001386266,0.003141158,0.00003750655,0.001923157,0.000001188354,0.0001998584,0.003045202,0.9547591,0.0009621209,0.0007806342,0.03457943],"study_design_scores_gemma":[0.0139525,0.03740048,0.01166021,0.0002272346,0.001562961,0.00006282108,0.00210475,0.06366935,0.7913462,0.009065318,0.06619002,0.002758165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5897421,0.001259166,0.4068106,0.0002611019,0.000375617,0.001262391,0.0002198743,0.00001126776,0.00005786049],"genre_scores_gemma":[0.9077614,0.00002415562,0.09071238,0.0001850358,0.0002531498,0.000596898,0.0003401601,0.00004452132,0.00008230442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3180193,"threshold_uncertainty_score":0.6068363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158604832505475,"score_gpt":0.2656652575393171,"score_spread":0.2498047742887696,"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."}}