{"id":"W4281298640","doi":"10.1101/2022.05.20.492818","title":"ExplaiNN: interpretable and transparent neural networks for genomics","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; BC Children's Hospital; Children's Hospital Foundation; University of British Columbia; Canadian Institute for Advanced Research","keywords":"Interpretability; Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Genomics; Machine learning; Sequence (biology); Sequence motif; Artificial neural network; Computational biology; Genome; Biology; Genetics","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":[0.000790738,0.001130226,0.0003801981,0.0004877302,0.0002348152,0.0008729201,0.001934941,0.001205261,0.004466692],"category_scores_gemma":[0.002815605,0.0006149295,0.0007099843,0.0003969947,0.0006423784,0.001297918,0.00132372,0.002210595,0.001469301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008462536,"about_ca_system_score_gemma":0.0007738892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003591699,"about_ca_topic_score_gemma":0.005275926,"domain_scores_codex":[0.999698,0.00006728008,0.0000135238,0.00008955014,0.0001107261,0.0000210225],"domain_scores_gemma":[0.9994337,0.0003027324,0.00005921448,0.0001114687,0.00006425476,0.00002862],"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":[0.0002611763,0.0001317585,0.001774618,0.0003689089,0.0001522436,0.0003157522,0.0001062262,0.6236189,0.02667687,0.05501036,0.04380377,0.2477795],"study_design_scores_gemma":[0.00001166356,0.000008208529,0.0001057856,0.00001188273,0.000004564162,0.00001390606,0.000003522897,0.974771,0.002461303,0.01873638,0.003864262,0.000007636379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00732613,0.0004437393,0.9695787,0.0006557469,0.0001099226,0.00005456632,0.001198235,0.01903926,0.001593635],"genre_scores_gemma":[0.2227754,0.0008364271,0.7610399,0.0007115233,0.0001197482,0.0003674442,0.005630948,0.002778474,0.005740117],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004466692,"threshold_uncertainty_score":0.01494253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01086614899654493,"score_gpt":0.2140195904769761,"score_spread":0.2031534414804312,"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."}}