{"id":"W4310594072","doi":"10.1093/bioinformatics/btac779","title":"DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Interpretability; Computer science; Artificial intelligence; Benchmark (surveying); Source code; Machine learning; Convolutional neural network; Deep learning; Code (set theory); 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":[0.0003890024,0.0002608655,0.0001904108,0.0001341252,0.0005581589,0.0001122937,0.0003547456,0.0001105437,0.0002190245],"category_scores_gemma":[0.00003980571,0.0002460299,0.0001129107,0.0002423191,0.00008158101,0.00002544441,0.0002718659,0.0004962923,0.00004655827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006399156,"about_ca_system_score_gemma":0.0001180876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006432938,"about_ca_topic_score_gemma":0.000004629638,"domain_scores_codex":[0.9982858,0.0001017808,0.0005532446,0.0002149073,0.0004719648,0.0003723188],"domain_scores_gemma":[0.9989248,0.0000173603,0.0004089841,0.0004352211,0.0001137364,0.00009989896],"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":[0.001339245,0.0007232109,0.01478118,0.00122279,0.0002278541,0.00001408621,0.002626536,0.8498707,0.1127407,0.0002567951,0.005571102,0.01062582],"study_design_scores_gemma":[0.002159092,0.002199239,0.0002979696,0.00005026471,0.00004855103,0.00004567998,0.002322951,0.8234739,0.02242374,0.000007744162,0.1463905,0.0005803395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.525099,0.0002540197,0.4620742,0.00009909624,0.0003358675,0.0009662776,0.00006512066,0.0001759736,0.01093041],"genre_scores_gemma":[0.9613082,0.00002266615,0.03505035,0.0001641312,0.0001407135,0.0001663876,0.001393483,0.00005114933,0.001702882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4362093,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004123358410521841,"score_gpt":0.1987609393383822,"score_spread":0.1946375809278603,"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."}}