{"id":"W2968249981","doi":"10.7717/peerj-cs.210","title":"Pay attention and you won’t lose it: a deep learning approach to sequence imputation","year":2019,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"","keywords":"Bottleneck; Computer science; Inference; Artificial intelligence; Machine learning; Imputation (statistics); Deep learning; Sequence (biology); Data mining; Missing data","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005230926,0.00019105,0.0001743043,0.0002369427,0.0004249253,0.0005190249,0.001420348,0.000040134,0.000002102147],"category_scores_gemma":[0.00003338245,0.0001626145,0.00003778435,0.001976463,0.000174337,0.00172481,0.0008050203,0.0002386202,0.0001818386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056991,"about_ca_system_score_gemma":0.00007089388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023511,"about_ca_topic_score_gemma":0.000001455195,"domain_scores_codex":[0.9973409,0.00005718182,0.0002573881,0.00119676,0.000629017,0.0005187918],"domain_scores_gemma":[0.998613,0.00008894137,0.0001319408,0.0006762919,0.0002279209,0.0002618899],"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":[0.00000518156,0.0000828774,0.004672309,0.00002892852,0.000005293348,0.000003550062,0.002170348,0.2687717,0.01676881,0.03467415,0.0001337605,0.6726831],"study_design_scores_gemma":[0.0001600364,0.0001080158,0.01749166,0.00001776278,0.00000236343,0.0000592145,0.0000207576,0.9787702,0.0002436845,0.001633904,0.001242014,0.0002504237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09211152,0.00002524857,0.9045819,0.001718825,0.0003221621,0.0004692312,4.935354e-7,0.000238602,0.0005320344],"genre_scores_gemma":[0.6043111,0.000006222273,0.3947909,0.0005910893,0.00006288758,0.0000300651,0.000002457826,0.000007347963,0.0001979122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7099984,"threshold_uncertainty_score":0.6631226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02097689253950328,"score_gpt":0.2766219643984715,"score_spread":0.2556450718589682,"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."}}