{"id":"W4415428083","doi":"10.1038/s41598-025-20812-1","title":"GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; Vector Institute; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Novo Nordisk","keywords":"Inference; Deep learning; Graph; Crosstalk; Transcriptome; Signalling; Cell; Cell type","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":[0.0006887246,0.00118551,0.0006724605,0.00150647,0.0004991716,0.0009976503,0.001023901,0.001546029,0.001653036],"category_scores_gemma":[0.002834367,0.000367063,0.001006525,0.0014096,0.0007303464,0.001038257,0.0006918579,0.001665951,0.000707675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559471,"about_ca_system_score_gemma":0.001216894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630714,"about_ca_topic_score_gemma":0.0289887,"domain_scores_codex":[0.9996381,0.0000845636,0.00001322078,0.0001565658,0.00005780188,0.00004974908],"domain_scores_gemma":[0.9985058,0.001016287,0.0001186648,0.0001350018,0.0001337182,0.00009057503],"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":[0.0002897217,0.0001944641,0.01353591,0.000226063,0.0001800164,0.0002743859,0.0000732374,0.868403,0.01201822,0.006717922,0.01578546,0.08230156],"study_design_scores_gemma":[0.00001019245,0.00002186747,0.0008025563,0.000005001246,0.000009943889,0.00002201336,0.00001058944,0.9916812,0.001599038,0.004991448,0.0008383208,0.000007846485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.359019,0.003065457,0.6048868,0.001883277,0.0002203075,0.0001503524,0.01686832,0.009831378,0.004075147],"genre_scores_gemma":[0.8358871,0.0007959178,0.1290251,0.0006123551,0.00008967219,0.0001788531,0.02828055,0.0003836368,0.004746766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01630714,"threshold_uncertainty_score":0.03242445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03579905010230983,"score_gpt":0.278463505434952,"score_spread":0.2426644553326421,"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."}}