{"id":"W4413236550","doi":"10.1016/j.imbio.2025.153035","title":"A multi-omic pipeline identifies complement as a driver of age-dependent progression in a model of multiple sclerosis","year":2025,"lang":"en","type":"article","venue":"Immunobiology","topic":"Complement system in diseases","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"","keywords":"Pipeline (software); Complement (music); Multiple sclerosis; Computational biology; Omics; Data science; Computer science; Biology; Neuroscience; Medicine; Bioinformatics; Immunology; Gene; Genetics; Phenotype","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285886,0.001389541,0.001237949,0.001387556,0.0008100109,0.001442784,0.0008683542,0.0009419117,0.001730161],"category_scores_gemma":[0.001503056,0.0004819227,0.002202254,0.0008796477,0.0003865055,0.0006948274,0.0009172925,0.001170812,0.0009448666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007753755,"about_ca_system_score_gemma":0.001964344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005460251,"about_ca_topic_score_gemma":0.008619796,"domain_scores_codex":[0.9995665,0.00005617046,0.00002135962,0.0001998123,0.0001007377,0.00005549482],"domain_scores_gemma":[0.9996118,0.0001209166,0.00005133522,0.00006164451,0.0001035471,0.00005079407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003808714,0.0008992823,0.07933266,0.002885716,0.003153431,0.001273137,0.0005446366,0.1182756,0.6002095,0.007733508,0.03714396,0.1447397],"study_design_scores_gemma":[0.0002343105,0.0007956512,0.03661861,0.0001366579,0.0008137877,0.0003458482,0.0002301263,0.8711187,0.05528105,0.01276377,0.02151354,0.0001479333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5078774,0.003429547,0.3520986,0.002107666,0.0004499565,0.0006762511,0.07895044,0.04883119,0.00557901],"genre_scores_gemma":[0.4541575,0.001568323,0.4264292,0.001001037,0.0001018674,0.00101749,0.1094105,0.003161826,0.003152208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005460251,"threshold_uncertainty_score":0.01085699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05000831684543724,"score_gpt":0.3139669776981186,"score_spread":0.2639586608526814,"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."}}