{"id":"W3042330596","doi":"10.1093/bioinformatics/btaa447","title":"EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Compute Canada; Genome Canada","keywords":"Sequence (biology); Context (archaeology); Computer science; Alignment-free sequence analysis; Inference; Mutation; Code (set theory); Probabilistic logic; Sequence learning; Artificial intelligence; Computational biology; Sequence alignment; Biology; Genetics; Peptide sequence; Programming language","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":[0.0006939747,0.0009602687,0.0006730382,0.0004325461,0.000358232,0.0008232539,0.002559208,0.00136953,0.006041832],"category_scores_gemma":[0.002662237,0.0005893739,0.0007578799,0.0005870194,0.000474756,0.001910436,0.0011355,0.00190695,0.001834324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009229572,"about_ca_system_score_gemma":0.001022832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006336411,"about_ca_topic_score_gemma":0.01045957,"domain_scores_codex":[0.9997957,0.0000417363,0.00001301368,0.00008506066,0.00004063456,0.00002385927],"domain_scores_gemma":[0.999589,0.000216806,0.00003184444,0.00004988637,0.00007837972,0.00003410986],"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":[0.000264605,0.00009168982,0.002063825,0.0002632579,0.0001497576,0.0002272994,0.000107897,0.8884596,0.009847216,0.008092942,0.01054094,0.07989096],"study_design_scores_gemma":[0.000008886595,0.00001553604,0.00008128364,0.000005898301,0.000006208768,0.00001679158,0.000003579234,0.9943509,0.001164621,0.00366851,0.0006730935,0.000004651088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1104487,0.001271665,0.8471849,0.001104602,0.0004361293,0.0001584657,0.007037281,0.02693562,0.005422597],"genre_scores_gemma":[0.7191204,0.0007015055,0.2601243,0.000561527,0.0001052677,0.0004692326,0.01010354,0.001774394,0.007039815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006336411,"threshold_uncertainty_score":0.02021194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0826937897695777,"score_gpt":0.2807156309608377,"score_spread":0.19802184119126,"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."}}