{"id":"W4313413436","doi":"10.1109/bibm55620.2022.9995071","title":"EMDS: predicting essential miRNAs based on deep learning and sequences","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Subsequence; Artificial intelligence; Computer science; Longest common subsequence problem; Support vector machine; Convolutional neural network; Deep learning; Machine learning; Artificial neural network; Pattern recognition (psychology); Algorithm; Mathematics","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":[0.0008212701,0.0009983919,0.0008101646,0.0009098055,0.0002874136,0.0004353223,0.001040592,0.0008487146,0.001334695],"category_scores_gemma":[0.00139131,0.000402319,0.0008717115,0.000447225,0.0002945553,0.000890061,0.0008045853,0.001147354,0.00038173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532098,"about_ca_system_score_gemma":0.001297622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00447896,"about_ca_topic_score_gemma":0.005391262,"domain_scores_codex":[0.9996458,0.00006802003,0.00003203658,0.00009223547,0.00009922939,0.00006259538],"domain_scores_gemma":[0.9995794,0.0002080919,0.00004766445,0.00002656711,0.0001023727,0.00003591532],"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":[0.0005826793,0.000430507,0.0146048,0.0002016188,0.0002008724,0.0003633723,0.00006385925,0.5567132,0.01972937,0.003749751,0.006273743,0.3970862],"study_design_scores_gemma":[0.00001224994,0.00003408119,0.0003839269,0.000004281705,0.000007999607,0.000027947,0.000004474557,0.9951017,0.003296881,0.0008217586,0.0003000345,0.000004592013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1896779,0.001739485,0.8001478,0.0005860396,0.0001708503,0.0001357057,0.0009868861,0.004453656,0.002101729],"genre_scores_gemma":[0.7543635,0.0007568079,0.2363368,0.0005222993,0.00008229127,0.0002127383,0.002865169,0.0001255444,0.004734741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00447896,"threshold_uncertainty_score":0.008905768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674005353689494,"score_gpt":0.2797197553463578,"score_spread":0.2629797018094629,"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."}}