{"id":"W1813034246","doi":"10.1186/1471-2105-6-23","title":"Recent Hits Acquired by BLAST (ReHAB): A tool to identify new hits in sequence similarity searches","year":2005,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Computer science; Similarity (geometry); Sequence (biology); Alignment-free sequence analysis; Information retrieval; Table (database); Interface (matter); Graphics; Data mining; Genome; Sequence alignment; Sequence database; Software; Multiple sequence alignment; Database; Artificial intelligence; Biology; Genetics; Programming language; Computer graphics (images)","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":[0.004892423,0.003257875,0.002096691,0.009282675,0.001296801,0.002283452,0.003356347,0.001687844,0.02659984],"category_scores_gemma":[0.01359904,0.001678265,0.001573092,0.005697097,0.0007803519,0.003728412,0.002983006,0.002426445,0.01877886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004972052,"about_ca_system_score_gemma":0.001211356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000636563,"about_ca_topic_score_gemma":0.001094407,"domain_scores_codex":[0.9974831,0.000788047,0.0004358469,0.0003817756,0.0007561299,0.0001551549],"domain_scores_gemma":[0.9948767,0.002974204,0.0006506983,0.000610587,0.0005552101,0.0003326302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004269455,0.0006264812,0.01061817,0.006485608,0.0007440762,0.003043346,0.001525706,0.00446184,0.06696562,0.01011735,0.4221869,0.4689554],"study_design_scores_gemma":[0.003177892,0.002241373,0.02354888,0.001792975,0.001063635,0.01419501,0.001113934,0.1195877,0.1693653,0.05822131,0.6044336,0.001258444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0233951,0.002472004,0.5407487,0.0009781206,0.0005882668,0.0009672167,0.03528636,0.3866465,0.008917797],"genre_scores_gemma":[0.04391591,0.001266624,0.8916011,0.0004747214,0.0001725854,0.001243897,0.04224214,0.01540213,0.00368089],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02659984,"threshold_uncertainty_score":0.08898526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03302620209898142,"score_gpt":0.3249614275038759,"score_spread":0.2919352254048945,"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."}}