{"id":"W2805090408","doi":"10.1038/s41598-018-26666-0","title":"Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Selection (genetic algorithm); Computer science; Gene selection; Sample (material); Deep learning; Pattern recognition (psychology); Identification (biology); Feature selection; Machine learning; Unsupervised learning; Microarray analysis techniques; Computational biology; Gene; Data mining; Biology; Gene expression; Genetics","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":[0.000345805,0.000109524,0.00008071378,0.0000859481,0.0003537702,0.0000988817,0.00007519303,0.00007426137,0.0003393798],"category_scores_gemma":[0.0001303169,0.0001046457,0.00004989749,0.0002084527,0.00009584252,0.000005200341,0.00003334699,0.00006874593,0.00001791777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003575678,"about_ca_system_score_gemma":0.0001059747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002795253,"about_ca_topic_score_gemma":0.00004395862,"domain_scores_codex":[0.9986985,0.00004522329,0.0002129077,0.0006270264,0.0002088042,0.0002075203],"domain_scores_gemma":[0.9991396,0.000005241793,0.0001971334,0.0004036244,0.000173432,0.000080958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003005643,0.00002604988,0.003926158,0.000005809472,0.000005584915,0.000002868102,0.00002727815,0.001128338,0.9828072,6.654337e-7,0.001741397,0.01029859],"study_design_scores_gemma":[0.00008186982,0.0000997401,0.004275734,0.00001595919,0.000007225677,0.0000149029,0.000008120745,0.01014188,0.7774233,0.00004470721,0.2077595,0.0001271105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8869482,0.00006046667,0.1071022,0.0001038919,0.004943935,0.0001350932,0.00000293075,0.00004459564,0.0006586687],"genre_scores_gemma":[0.9956468,0.000007365169,0.0008168906,0.0001106768,0.0005171246,0.00005566165,0.0002058782,0.00001727905,0.002622306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2060181,"threshold_uncertainty_score":0.4267327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519251277270109,"score_gpt":0.2679066461971449,"score_spread":0.2527141334244438,"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."}}