{"id":"W2609824936","doi":"10.1109/icpr.2016.7899956","title":"Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lasso (programming language); Elastic net regularization; Neuroimaging; Regression; Voxel; Computer science; Artificial intelligence; Regularization (linguistics); Multivariate statistics; Pattern recognition (psychology); Machine learning; Mathematics; Statistics; Feature selection; Psychology; Neuroscience","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":[0.002937078,0.0009595818,0.001230385,0.001041854,0.000366715,0.0007661737,0.001281673,0.001375062,0.001327196],"category_scores_gemma":[0.006155162,0.0006385174,0.00128432,0.001845561,0.000857897,0.001071355,0.001135806,0.001699639,0.0006100118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006560294,"about_ca_system_score_gemma":0.001541378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003632949,"about_ca_topic_score_gemma":0.003602901,"domain_scores_codex":[0.9990533,0.000478775,0.00003675143,0.0001617868,0.0002239287,0.00004545394],"domain_scores_gemma":[0.9976521,0.001669584,0.0002716172,0.000132298,0.0002267861,0.00004759647],"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":[0.0001403923,0.00008137497,0.0007265632,0.0002276683,0.0001489646,0.0001456974,0.00008104055,0.8337389,0.008494797,0.02413364,0.004587757,0.1274932],"study_design_scores_gemma":[0.000008695891,0.00001415443,0.0001009334,0.000005966433,0.000007038816,0.0000163413,0.000003879242,0.9908454,0.0007152359,0.007587856,0.0006881638,0.000006271006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002662452,0.000306948,0.9961571,0.0002383109,0.00001514732,0.00001609653,0.00008431551,0.0002816275,0.0002380331],"genre_scores_gemma":[0.1869851,0.001896279,0.8060197,0.000371338,0.0002356839,0.0003334732,0.00125395,0.0002468259,0.002657565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003632949,"threshold_uncertainty_score":0.01553297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05599274891812051,"score_gpt":0.3253803174776034,"score_spread":0.2693875685594829,"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."}}