{"id":"W2202472262","doi":"10.1371/journal.pone.0144782","title":"SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Research Foundation of Korea; Banting and Best Diabetes Centre, University of Toronto; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; National Research Foundation","keywords":"Artificial intelligence; Computer science; Support vector machine; Machine learning; Discriminative model; Leverage (statistics); Probabilistic logic; Sequence motif; Pattern recognition (psychology); Computational biology; Algorithm; Biology; DNA; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007119943,0.0005560478,0.0006091647,0.0007550364,0.0002536495,0.0005486322,0.001089359,0.001038591,0.00232264],"category_scores_gemma":[0.002158165,0.00031713,0.0005149907,0.0005201961,0.0003505794,0.0008548256,0.0007060576,0.001022478,0.000922283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003164131,"about_ca_system_score_gemma":0.0007586509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853636,"about_ca_topic_score_gemma":0.002534548,"domain_scores_codex":[0.9996903,0.00007314433,0.00001319409,0.0001088113,0.00007586052,0.00003860034],"domain_scores_gemma":[0.9993367,0.0003153079,0.00008860776,0.00008624321,0.0001068848,0.00006632478],"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":[0.0005498924,0.0002498675,0.01351501,0.0002719072,0.0001207411,0.0003522843,0.0001188313,0.5215747,0.05093334,0.01816422,0.008650399,0.3854988],"study_design_scores_gemma":[0.00000407012,0.00002062639,0.0002746797,0.000002855702,0.000002231914,0.00003137864,0.000006221617,0.9947217,0.002405411,0.001988074,0.0005391642,0.000003646738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07867787,0.0002058796,0.9169863,0.0002024158,0.00006104403,0.00003239533,0.0003051037,0.002641809,0.0008871817],"genre_scores_gemma":[0.6570365,0.000158772,0.3378195,0.0001671729,0.00005941039,0.00009907377,0.001461471,0.0003016602,0.002896324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00232264,"threshold_uncertainty_score":0.007770002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04510598078314133,"score_gpt":0.2286483510283435,"score_spread":0.1835423702452022,"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."}}