{"id":"W4382631507","doi":"10.1093/bioinformatics/btad250","title":"PlasBin-flow: a flow-based MILP algorithm for plasmid contigs binning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; European Commission; Genome Canada","keywords":"Contig; Plasmid; Genetics; Biology; Computer science; Computational biology; Sequence assembly; Genome; Algorithm; DNA; 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":[0.00165042,0.002466119,0.001408025,0.001633406,0.0009415882,0.001757708,0.001859654,0.001652301,0.0129206],"category_scores_gemma":[0.004540192,0.001138858,0.001765039,0.001575457,0.0008843275,0.001556009,0.001986924,0.002482048,0.002841129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190274,"about_ca_system_score_gemma":0.002828695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007395631,"about_ca_topic_score_gemma":0.009456426,"domain_scores_codex":[0.9993963,0.0001512283,0.00003768631,0.0001827728,0.0001426973,0.00008928923],"domain_scores_gemma":[0.998292,0.00118014,0.0001127914,0.0001067245,0.0002123007,0.00009598904],"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":[0.0002250109,0.0001850034,0.001652339,0.0003958039,0.00009018314,0.0001479204,0.0001263311,0.823683,0.003358477,0.009567188,0.01659076,0.1439779],"study_design_scores_gemma":[0.00004170712,0.00002687755,0.0000898013,0.00002080619,0.000008888872,0.00002043917,0.00002030715,0.9866933,0.0008708005,0.009198483,0.003000086,0.000008511178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007458732,0.0003391424,0.979355,0.0002440365,0.0000898962,0.0001842121,0.001207399,0.00884126,0.002280268],"genre_scores_gemma":[0.06141619,0.0001958909,0.9286713,0.000220619,0.00005791877,0.0005153383,0.004707208,0.002004795,0.002210755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0129206,"threshold_uncertainty_score":0.04322368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611820032584436,"score_gpt":0.2409062830554634,"score_spread":0.224788082729619,"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."}}