{"id":"W4394763083","doi":"10.1093/bib/bbae160","title":"SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; University of Windsor","keywords":"Computer science; Embedding; Graph; ENCODE; Convolutional neural network; Similarity (geometry); Data mining; Computational biology; Artificial intelligence; Theoretical computer science; Gene; Biology; Genetics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004258797,0.001401114,0.0007262944,0.001554536,0.0003537789,0.0004622212,0.0009408407,0.0008926743,0.002031838],"category_scores_gemma":[0.00204302,0.0003337099,0.0009344336,0.001426062,0.0003537499,0.0007241364,0.0006494735,0.001248309,0.0007885318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007351625,"about_ca_system_score_gemma":0.001137018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413866,"about_ca_topic_score_gemma":0.0230986,"domain_scores_codex":[0.9996926,0.00006001905,0.00001206031,0.0001306502,0.00007106181,0.00003378171],"domain_scores_gemma":[0.9994085,0.0002627669,0.00008934663,0.00008955221,0.0001028299,0.00004702555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007004811,0.00037487,0.02906558,0.000641043,0.0007073387,0.000461082,0.0002178975,0.5094763,0.05692038,0.009027164,0.05588838,0.3365195],"study_design_scores_gemma":[0.00002064181,0.00002879107,0.001992933,0.000008022747,0.00002089183,0.00004923291,0.00001740902,0.9869868,0.004440055,0.0040837,0.002335954,0.00001564433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1331994,0.001950946,0.8271474,0.0006415274,0.0001922638,0.000214717,0.01586966,0.01827466,0.002509397],"genre_scores_gemma":[0.6664017,0.0009975535,0.2803184,0.0003843407,0.0001124438,0.0005262516,0.04456834,0.0009741692,0.005716827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01413866,"threshold_uncertainty_score":0.02811277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171838474383565,"score_gpt":0.2506625122960691,"score_spread":0.2389441275522334,"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."}}