{"id":"W3062065023","doi":"10.1007/978-3-030-57821-3_16","title":"MiRNA-Disease Associations Prediction Based on Negative Sample Selection and Multi-layer Perceptron","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Semantic similarity; Computer science; Similarity (geometry); Artificial intelligence; microRNA; Perceptron; Computational biology; Kernel (algebra); Disease; Selection (genetic algorithm); Machine learning; Data mining; Bioinformatics; Biology; Medicine; Mathematics; Artificial neural network; Gene; Genetics; Pathology","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":[0.003325847,0.001344294,0.001585975,0.0008312422,0.0004715432,0.001106329,0.001867489,0.001433492,0.002775073],"category_scores_gemma":[0.003306232,0.0006832184,0.001646192,0.0007904635,0.0005168887,0.001352374,0.001069727,0.001722302,0.001172413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004872372,"about_ca_system_score_gemma":0.0008189923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002342249,"about_ca_topic_score_gemma":0.003394605,"domain_scores_codex":[0.9990231,0.0002887434,0.00007436232,0.0002768297,0.0001879257,0.0001491074],"domain_scores_gemma":[0.9986175,0.0009540467,0.0000572031,0.00008746838,0.0002297542,0.00005397401],"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":[0.001277496,0.0005600037,0.008900787,0.0002503801,0.0004631723,0.000380156,0.00008626687,0.1829912,0.01991306,0.003130526,0.00907177,0.7729751],"study_design_scores_gemma":[0.00001758675,0.00005221842,0.0007285859,0.000007638293,0.00004696399,0.00005413797,0.000005072904,0.9958923,0.001758908,0.001158776,0.0002684847,0.000009381302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07932346,0.00256743,0.910737,0.0005792212,0.000329129,0.00009417926,0.0004018846,0.003323584,0.00264409],"genre_scores_gemma":[0.7481447,0.0009582696,0.238626,0.0005789854,0.0003397756,0.0001874518,0.001501956,0.0001987879,0.009464075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003325847,"threshold_uncertainty_score":0.01758897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754989936869606,"score_gpt":0.2534710729879798,"score_spread":0.2359211736192838,"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."}}