{"id":"W2996936198","doi":"10.1186/s12920-019-0598-0","title":"A network clustering based feature selection strategy for classifying autism spectrum disorder","year":2019,"lang":"en","type":"article","venue":"BMC Medical Genomics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Autism spectrum disorder; Subnetwork; Pattern recognition (psychology); Feature selection; Default mode network; Cluster analysis; Functional magnetic resonance imaging; Non-negative matrix factorization; Linear discriminant analysis; Machine learning; Feature (linguistics); Neuroimaging; Matrix decomposition; Autism; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004502145,0.0001857021,0.0002397893,0.00005483002,0.0002943421,0.0000584174,0.000213698,0.0001915107,0.0001781681],"category_scores_gemma":[0.002723003,0.0001748415,0.0001065288,0.0002557935,0.00007417852,0.00008942586,0.0001067949,0.0003251707,0.00006451324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172774,"about_ca_system_score_gemma":0.0004550767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001195542,"about_ca_topic_score_gemma":0.00106557,"domain_scores_codex":[0.9982252,0.000119122,0.0001978968,0.0005805356,0.0003911736,0.0004860804],"domain_scores_gemma":[0.9944197,0.005155367,0.00009194183,0.0001848449,0.00001412745,0.0001340097],"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":[0.001820803,0.000439159,0.04977583,0.001133933,0.00008627209,0.00002088378,0.0002629107,0.8261179,0.02504521,0.04463055,0.03970212,0.0109644],"study_design_scores_gemma":[0.001948944,0.0003831384,0.008043081,0.00007865842,0.00002108275,0.00003530376,0.00005817714,0.9269933,0.0008092358,0.004667376,0.0565684,0.0003933586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2626168,0.0002450264,0.703643,0.02463878,0.00409763,0.001783433,0.00003023053,0.0003880977,0.002556991],"genre_scores_gemma":[0.9877425,0.00002058133,0.003705343,0.006193512,0.000781834,0.00008106106,0.000007557556,0.00004708965,0.001420508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7251257,"threshold_uncertainty_score":0.712983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04734142244381935,"score_gpt":0.2748786694605475,"score_spread":0.2275372470167282,"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."}}