{"id":"W4384663316","doi":"10.1186/s12859-023-05390-1","title":"IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods","year":2023,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"University of Pennsylvania","keywords":"Pipeline (software); Computer science; Workflow; Data mining; Computational biology; Fragment (logic); Identifier; Process (computing); Pipeline transport; Biology; Database; Engineering; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007696669,0.0002590638,0.000320632,0.0004306039,0.0001989134,0.000122853,0.0003312598,0.0001788551,0.00001780184],"category_scores_gemma":[0.0001758478,0.0002093274,0.0002248572,0.001501587,0.0001335226,0.00003349172,0.00008224144,0.0001131177,0.00008524927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003108742,"about_ca_system_score_gemma":0.0001670823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000551034,"about_ca_topic_score_gemma":0.0002191537,"domain_scores_codex":[0.9983262,0.00008273171,0.0006407612,0.0002413757,0.0003069306,0.0004019475],"domain_scores_gemma":[0.9980801,0.0001417804,0.0003257311,0.0007417461,0.0006026024,0.0001080803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01085599,0.0004187673,0.007290193,0.002882398,0.0040431,0.000001894177,0.008913212,0.08846424,0.5842448,0.0007869698,0.05451735,0.2375811],"study_design_scores_gemma":[0.000684855,0.0007781977,0.0005004343,0.00001627914,0.0001608885,0.000002790604,0.001230517,0.8196583,0.1724084,0.00006224945,0.004230315,0.0002667318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08045192,0.0001424853,0.9179813,0.0001740907,0.00005321665,0.0007524244,0.0002024936,0.00007976309,0.0001622961],"genre_scores_gemma":[0.03953131,0.000437364,0.9504687,0.0007959342,0.0001427739,0.0002418542,0.006985638,0.00005975007,0.001336736],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7311941,"threshold_uncertainty_score":0.8536124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08634068336102907,"score_gpt":0.3998976343996767,"score_spread":0.3135569510386477,"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."}}