{"id":"W2133554582","doi":"10.1186/1471-2105-13-326","title":"A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers","year":2012,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Institute of Infection and Immunity; St. Paul's Hospital; Simon Fraser University; University of British Columbia; Prevention of Organ Failure","funders":"Astellas Pharma; Genome British Columbia; St. Paul's Foundation; Novartis Pharma; Genome Canada","keywords":"Pipeline (software); Computer science; Ensemble learning; Artificial intelligence; Machine learning; Random subspace method; Biomarker discovery; DNA microarray; Biomarker; Data mining; Big data; Receiver operating characteristic; Pattern recognition (psychology); Support vector machine; Proteomics; Biology; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00307755,0.00130466,0.001326708,0.001599651,0.0009206342,0.001848835,0.002108919,0.001131748,0.004422603],"category_scores_gemma":[0.008388014,0.0009229404,0.001799217,0.001425622,0.0004968951,0.001446441,0.001847375,0.002023856,0.001483921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361027,"about_ca_system_score_gemma":0.002740706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009265159,"about_ca_topic_score_gemma":0.01058762,"domain_scores_codex":[0.9989622,0.0003057748,0.0001066021,0.0002272477,0.0003135246,0.00008464532],"domain_scores_gemma":[0.9967314,0.001978091,0.0001667125,0.0003621965,0.0006595525,0.0001020568],"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":[0.0002231679,0.0001994287,0.00343186,0.0002119032,0.0002855053,0.0002273591,0.0001818821,0.6615179,0.005011594,0.01306673,0.005951985,0.3096907],"study_design_scores_gemma":[0.0000158463,0.0000224269,0.0002557526,0.000008948309,0.00001789531,0.00002236067,0.00001215104,0.9883727,0.001179373,0.008935855,0.001146391,0.00001031136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004945538,0.00008118815,0.9910774,0.0001804494,0.00001764014,0.0001609431,0.0002731027,0.002691821,0.000571798],"genre_scores_gemma":[0.07598902,0.0001039848,0.9210868,0.0001177185,0.00002542977,0.0006295115,0.001052005,0.0001672895,0.0008281535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009265159,"threshold_uncertainty_score":0.01842242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04168584868854552,"score_gpt":0.2975414227229967,"score_spread":0.2558555740344512,"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."}}