{"id":"W4393060203","doi":"10.1002/biot.202300684","title":"Proteomic workflows for deep phenotypic profiling of 3D organotypic liver models","year":2024,"lang":"en","type":"article","venue":"Biotechnology Journal","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovative Medicines Initiative; Alexander S. Onassis Public Benefit Foundation; Vetenskapsrådet; Kungliga Tekniska Högskolan; Knut och Alice Wallenbergs Stiftelse; European Commission; European Federation of Pharmaceutical Industries and Associations; McGill University; Diamond Light Source; Robert Bosch Stiftung","keywords":"Computational biology; Biomarker discovery; Phenotype; Proteomics; Biology; Workflow; Drug discovery; Systems biology; Proteome; Bioinformatics; Computer science; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001566854,0.001119057,0.0008403983,0.001212051,0.0005960185,0.001292189,0.0006442871,0.0006861224,0.001663143],"category_scores_gemma":[0.001046157,0.0005726407,0.001088307,0.0008281261,0.0003712794,0.0007294686,0.001233163,0.001193373,0.001686748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004758439,"about_ca_system_score_gemma":0.0007056971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006627612,"about_ca_topic_score_gemma":0.001421408,"domain_scores_codex":[0.999144,0.0001440608,0.0001282997,0.0002212891,0.0002799224,0.00008245283],"domain_scores_gemma":[0.9993499,0.0001344598,0.000101459,0.0001863173,0.0001565276,0.00007133569],"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":[0.0001048602,0.00003574919,0.0004801447,0.0001137918,0.00002716998,0.00008830989,0.0000514434,0.0008507088,0.9928316,0.0002304265,0.0002714637,0.004914291],"study_design_scores_gemma":[0.00001967446,0.0001685026,0.00796182,0.00004108143,0.00006404397,0.0004671144,0.00008154909,0.01291094,0.9685665,0.0008050441,0.008849104,0.00006471745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1860316,0.001263034,0.7923245,0.0002588003,0.0001783124,0.00102254,0.01100129,0.005625404,0.002294557],"genre_scores_gemma":[0.2281933,0.002790082,0.7473766,0.0002719213,0.00005017318,0.003142563,0.01510133,0.001246435,0.001827618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001663143,"threshold_uncertainty_score":0.008286417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406236962173998,"score_gpt":0.2428104468851713,"score_spread":0.2287480772634313,"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."}}