{"id":"W4391257190","doi":"10.1002/1878-0261.13588","title":"Deciphering metabolic crosstalk in context: lessons from inflammatory diseases","year":2024,"lang":"en","type":"article","venue":"Molecular Oncology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"","keywords":"Metabolomics; Biology; Computational biology; Immune system; Cellular metabolism; Mass cytometry; Context (archaeology); Crosstalk; Cell metabolism; Bioinformatics; Metabolome; Multicellular organism; Disease; Cell; Metabolism; Immunology; Medicine; Phenotype; Biochemistry; Pathology","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":[0.001346723,0.00100992,0.001475784,0.001268376,0.0004919706,0.002520773,0.0007333898,0.001926686,0.00160508],"category_scores_gemma":[0.001473139,0.0003220067,0.001013699,0.0009715179,0.001666813,0.004601934,0.001634263,0.005588406,0.0005846072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126434,"about_ca_system_score_gemma":0.001317288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001374069,"about_ca_topic_score_gemma":0.003031846,"domain_scores_codex":[0.999709,0.00007418259,0.00003027192,0.00007353338,0.00005830638,0.00005476484],"domain_scores_gemma":[0.99918,0.0003917954,0.00006881449,0.00005797669,0.0001543839,0.0001469456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008737965,0.0002088596,0.008291715,0.01215946,0.0005807834,0.003772343,0.002140847,0.005133179,0.07418596,0.09288145,0.05519089,0.7445807],"study_design_scores_gemma":[0.00007652269,0.0007013079,0.01192715,0.00499894,0.0005040817,0.005493103,0.003348008,0.004039831,0.0244449,0.2413906,0.7028591,0.0002165061],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01701291,0.897827,0.02315135,0.05211551,0.003160313,0.00003421943,0.000376673,0.0002014249,0.006120604],"genre_scores_gemma":[0.07303048,0.8899621,0.01683632,0.01280584,0.004362233,0.0000526494,0.0002741067,0.00008429261,0.002592014],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002520773,"threshold_uncertainty_score":0.008172929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224581733109267,"score_gpt":0.2875637375505167,"score_spread":0.275317920219424,"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."}}