{"id":"W3089258710","doi":"10.3390/genes11101127","title":"Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes","year":2020,"lang":"en","type":"article","venue":"Genes","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Waseda University; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Latent Dirichlet allocation; Interpretability; Dirichlet distribution; Computer science; Mutation; Hyperparameter; Signature (topology); Hierarchical Dirichlet process; Prior probability; Pattern recognition (psychology); Bayesian probability; Artificial intelligence; Computational biology; Genetics; Topic model; Mathematics; Biology","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":[0.003703575,0.000855718,0.00109823,0.001513133,0.0008993316,0.001638491,0.001436432,0.001101214,0.00199953],"category_scores_gemma":[0.01052107,0.0007930003,0.0016013,0.001124039,0.001290099,0.00213251,0.001864769,0.001806199,0.000539165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001794958,"about_ca_system_score_gemma":0.001654616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007624832,"about_ca_topic_score_gemma":0.008283157,"domain_scores_codex":[0.9979095,0.001006052,0.000075767,0.00059975,0.000231862,0.000177095],"domain_scores_gemma":[0.9956541,0.002953057,0.0003157685,0.0005525924,0.0003830121,0.0001414859],"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":[0.0008591529,0.0001920001,0.0136107,0.0001567249,0.0002699533,0.0002540085,0.0008186313,0.7204695,0.01389662,0.05937317,0.005221639,0.1848778],"study_design_scores_gemma":[0.00001904606,0.00001154469,0.0006341796,0.000004263808,0.00001066088,0.00002890047,0.0000223238,0.9706827,0.000967013,0.02687743,0.0007285603,0.00001349459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04678232,0.0002073667,0.9503962,0.0004293347,0.00002985627,0.00005371684,0.0003675721,0.0008599991,0.0008737103],"genre_scores_gemma":[0.636025,0.0003253173,0.3570648,0.0003464895,0.0001223129,0.0003069191,0.001641713,0.0003840541,0.003783609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007624832,"threshold_uncertainty_score":0.01958662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809783250445052,"score_gpt":0.2689681632980901,"score_spread":0.2508703307936396,"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."}}