{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009955633,0.001077322,0.0005838004,0.000487377,0.0003575283,0.0005646704,0.0009681511,0.001064579,0.001744742],"category_scores_gemma":[0.002523521,0.0004429311,0.0005357364,0.0005330319,0.0004525492,0.0007696842,0.0005911089,0.001154287,0.0002744188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180998,"about_ca_system_score_gemma":0.001348395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008580235,"about_ca_topic_score_gemma":0.008104019,"domain_scores_codex":[0.9996735,0.00008665423,0.00001949509,0.00009597275,0.00008385866,0.00004048826],"domain_scores_gemma":[0.9993386,0.0003774501,0.00006090858,0.00002722665,0.0001730046,0.00002286212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004494749,0.00003824763,0.0004991099,0.000050593,0.00002362662,0.00003464988,0.0000260345,0.9423866,0.002643379,0.002705873,0.0004825844,0.0510643],"study_design_scores_gemma":[0.000001358054,0.000006434626,0.00002644681,0.00000138127,0.000001785867,0.000002429395,0.000001074218,0.9994029,0.0002588649,0.0002390443,0.00005698907,0.000001243498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03327204,0.000235714,0.9635227,0.0001346643,0.00003525042,0.000054793,0.00003178457,0.0004276517,0.002285388],"genre_scores_gemma":[0.5821576,0.000271713,0.4124196,0.0001742293,0.00003228735,0.0003519827,0.0002067112,0.0001334533,0.00425238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008580235,"threshold_uncertainty_score":0.01706058,"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."}}