{"id":"W4383340501","doi":"10.1371/journal.pcbi.1011224","title":"Ten quick tips for avoiding pitfalls in multi-omics data integration analyses","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Alan Turing Institute","keywords":"Phenomics; Data integration; Omics; Computer science; Data science; Context (archaeology); Biological data; Computational biology; Bioinformatics; Genomics; Data mining; Biology; Genome","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":[0.000350589,0.0001156756,0.0001536315,0.000113814,0.00008186197,0.00002055952,0.0002952015,0.0001564838,0.000003539383],"category_scores_gemma":[0.0002066728,0.000106043,0.00004377847,0.0001537839,0.00005007178,0.000007464357,0.0002212502,0.00009549748,0.00002443941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001577292,"about_ca_system_score_gemma":0.00007053599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001281311,"about_ca_topic_score_gemma":0.00008069868,"domain_scores_codex":[0.9990682,0.00005739712,0.0003096768,0.0003007122,0.00004779884,0.0002162063],"domain_scores_gemma":[0.9994078,0.0001416726,0.0000978839,0.0002354552,0.00008071588,0.00003642771],"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":[0.0005689542,0.0005786168,0.0206642,0.000198357,0.0009361224,0.000005602831,0.0008678729,0.2039599,0.6666111,0.01673212,0.0354496,0.05342756],"study_design_scores_gemma":[0.0008553594,0.0001335268,0.002759114,0.00001421482,0.00001839318,0.000004325058,0.0001547081,0.9834081,0.002281518,0.007333577,0.002828329,0.0002088577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3424242,0.0002819813,0.6551292,0.0006121011,0.0002603765,0.0004963482,0.0006421127,0.00004170416,0.0001119639],"genre_scores_gemma":[0.951935,0.00007192456,0.02667581,0.0003660936,0.0001758027,0.00003348122,0.02065009,0.00001482778,0.0000770019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7794482,"threshold_uncertainty_score":0.4324309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1487011156668009,"score_gpt":0.3739861326330575,"score_spread":0.2252850169662566,"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."}}