{"id":"W2952663747","doi":"10.1101/563601","title":"Deep learning in bioinformatics: introduction, application, and perspective in big data era","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Community Based Research Centre; Kootenay Association for Science & Technology","funders":"King Abdullah University of Science and Technology","keywords":"Deep learning; Artificial intelligence; Computer science; Interpretability; Big data; Autoencoder; Machine learning; Field (mathematics); Deep neural networks; Overfitting; Convolutional neural network; Data science; Artificial neural network; Implementation; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001049473,0.0004383886,0.0004393334,0.0003871402,0.00007800478,0.0001461174,0.0007942638,0.0005788432,0.000006170111],"category_scores_gemma":[0.0009793225,0.0004950858,0.00004457739,0.0003857591,0.0001257229,0.00003253263,0.001691919,0.001264122,0.00003490209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745122,"about_ca_system_score_gemma":0.0003847655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001453179,"about_ca_topic_score_gemma":0.00006099795,"domain_scores_codex":[0.9975425,0.0001416886,0.0006999964,0.0009608784,0.000232792,0.0004221307],"domain_scores_gemma":[0.9970793,0.00002982296,0.0004826205,0.001989775,0.0003096552,0.0001087969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003959355,0.0006584616,0.5383324,0.004214364,0.0006382611,0.00002218772,0.000928172,0.08160937,0.3675614,0.002683867,0.002333471,0.000622073],"study_design_scores_gemma":[0.003590809,0.0003582918,0.4032636,0.0005500902,0.0001475479,5.436021e-7,0.0004600287,0.4847804,0.02743702,0.00002007215,0.07604499,0.00334666],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9154733,0.00393287,0.07514896,0.001405599,0.001386245,0.002182865,0.0001421253,0.0001999073,0.000128108],"genre_scores_gemma":[0.9845994,0.000959308,0.01317813,0.0001535545,0.0009024522,0.00009288637,0.00002239011,0.00008113046,0.00001072885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.403171,"threshold_uncertainty_score":0.9997501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009148252838469676,"score_gpt":0.230911299612002,"score_spread":0.2217630467735323,"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."}}