{"id":"W2912802963","doi":"10.1109/tcbb.2019.2897679","title":"A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Centrality; Artificial intelligence; Machine learning; Biological network; Identification (biology); Feature (linguistics); Embedding; Exploit; Representation (politics); Feature learning; Biological data; Support vector machine; Function (biology); Data mining; Computational biology; Bioinformatics; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002251852,0.0001686628,0.0002036546,0.00008943053,0.0001861049,0.0000376055,0.0001554187,0.0003285471,0.0000159964],"category_scores_gemma":[0.00009846565,0.0001419062,0.0001010824,0.0000933089,0.0001150235,0.00003647918,0.00001652241,0.0002175915,0.00001332815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001112765,"about_ca_system_score_gemma":0.00003835831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000510119,"about_ca_topic_score_gemma":0.000003528395,"domain_scores_codex":[0.9990329,0.00003511853,0.0005175511,0.0001336428,0.00008150653,0.0001992372],"domain_scores_gemma":[0.9991443,0.0002408299,0.0002791015,0.0001440541,0.0001408401,0.00005092673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002353821,0.0005627255,0.006593091,0.001594619,0.001344445,3.164141e-7,0.00494816,0.3864173,0.0628332,0.01778497,0.0005240598,0.5150433],"study_design_scores_gemma":[0.002661016,0.002739137,0.0004775899,0.0001766621,0.00006648554,0.00002847515,0.001782435,0.9416687,0.0253871,0.0189744,0.005319996,0.0007180056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1034348,0.00008611708,0.8955114,0.00005941264,0.0002413433,0.0004677086,0.0001146453,0.00001745838,0.00006713526],"genre_scores_gemma":[0.8019174,0.00007465207,0.1970517,0.0001781874,0.0000375159,0.00003320395,0.0006836117,0.000006670138,0.0000170211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6984826,"threshold_uncertainty_score":0.5786767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281912414583567,"score_gpt":0.26978390858721,"score_spread":0.2569647844413743,"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."}}