{"id":"W2076666663","doi":"10.1186/1742-4690-5-110","title":"HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels","year":2008,"lang":"en","type":"article","venue":"Retrovirology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Support vector machine; Computer science; String kernel; Human immunodeficiency virus (HIV); Classifier (UML); CXCR4; Artificial intelligence; Kernel (algebra); String (physics); Computational biology; Machine learning; Bioinformatics; Kernel method; Biology; Radial basis function kernel; Virology; Chemokine; Mathematics; Genetics; Combinatorics; Receptor","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":[0.0001528197,0.0001176578,0.0001550783,0.00004812219,0.00007044247,0.000003255,0.0001731853,0.0002020463,0.00001966731],"category_scores_gemma":[0.0001112638,0.0001185389,0.0000443611,0.00005546732,0.0001333331,0.000008666417,0.00007198902,0.0001098903,0.00001862393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001789671,"about_ca_system_score_gemma":0.00002649214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002800705,"about_ca_topic_score_gemma":0.000006394939,"domain_scores_codex":[0.9991129,0.00007516948,0.0002876567,0.0002227434,0.0001214071,0.0001801394],"domain_scores_gemma":[0.9992964,0.00001384911,0.0002067263,0.0003732292,0.00004811883,0.00006162266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008190516,0.00005427106,0.6272784,0.00001456166,0.00002369395,3.409123e-7,0.0001017232,0.0009740302,0.3703313,0.00003911644,0.0002182802,0.0008822873],"study_design_scores_gemma":[0.003375497,0.003370612,0.5515557,0.00001431191,0.0000651949,0.0001966125,0.0001373361,0.09669944,0.2924029,0.00004970263,0.05160529,0.0005274583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316581,0.00003591218,0.06747454,0.00001834475,0.0000801697,0.0001853838,0.00004472373,0.00002659227,0.0004761716],"genre_scores_gemma":[0.9947369,0.00006412841,0.004115896,0.00009883996,0.000150005,0.00002172542,0.0005644442,0.00001814252,0.0002298939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09572541,"threshold_uncertainty_score":0.4833875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009683524912007248,"score_gpt":0.2447139313064987,"score_spread":0.2350304063944914,"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."}}