{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001360464,0.000586141,0.0007765716,0.001355588,0.0003182716,0.0008564648,0.0007811219,0.0007577333,0.0009156844],"category_scores_gemma":[0.003444156,0.0002180973,0.0006021719,0.001218156,0.0002648942,0.001488961,0.0008868357,0.0006607005,0.0005377124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004948878,"about_ca_system_score_gemma":0.000535883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001965347,"about_ca_topic_score_gemma":0.001085181,"domain_scores_codex":[0.9992961,0.0001980411,0.00009110344,0.0001393631,0.0002055975,0.00006971547],"domain_scores_gemma":[0.9980731,0.0009833938,0.0002733071,0.0002255755,0.0003467171,0.00009784446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001801816,0.0006355733,0.0362883,0.0002864718,0.0003755071,0.0004767361,0.0001967705,0.3243286,0.04241578,0.006096048,0.003500961,0.5835974],"study_design_scores_gemma":[0.000007607751,0.00007356508,0.00193708,0.000006055429,0.00002011447,0.00007336645,0.00001868403,0.9910705,0.004923841,0.00138447,0.0004763311,0.000008411935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4494575,0.0008425718,0.5440844,0.0002184072,0.00006045972,0.00008129323,0.0004301476,0.003151644,0.001673583],"genre_scores_gemma":[0.8868951,0.0002177279,0.1111928,0.00003635175,0.00001771203,0.00003400514,0.000688882,0.00008409181,0.0008334316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001965347,"threshold_uncertainty_score":0.007194877,"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."}}