{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007513819,0.001509792,0.0008676085,0.0009237393,0.000327644,0.0009566111,0.002051848,0.001372086,0.002909031],"category_scores_gemma":[0.001984746,0.0004843093,0.001124611,0.0006385138,0.0004942378,0.001296923,0.001174022,0.001916233,0.001133701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271091,"about_ca_system_score_gemma":0.001104128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005444736,"about_ca_topic_score_gemma":0.008265897,"domain_scores_codex":[0.9997403,0.00003922027,0.00001256655,0.0001063137,0.00006353117,0.0000380692],"domain_scores_gemma":[0.9994259,0.0002643281,0.00006789464,0.00008714611,0.00009962511,0.000055189],"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":[0.0005398793,0.0003935287,0.01095624,0.0005983058,0.0003707978,0.0004815718,0.0001232481,0.708298,0.02773561,0.0122735,0.0395223,0.198707],"study_design_scores_gemma":[0.00001375837,0.00002507088,0.0002622598,0.000007858321,0.00001140551,0.00002525656,0.000004522912,0.9917015,0.003068161,0.004063373,0.0008097254,0.000007067867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1673307,0.002852782,0.7769747,0.001319976,0.0002938164,0.0002084028,0.01129621,0.03495311,0.004770443],"genre_scores_gemma":[0.6859388,0.001147682,0.2683476,0.001154377,0.0001735801,0.0004318097,0.03398262,0.0009967057,0.007826785],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005444736,"threshold_uncertainty_score":0.01082611,"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."}}