{"id":"W7133015063","doi":"","title":"Developing A Graph-based Deep Learning Method to Predict Cell-Cell Communication in Single-cell RNAseq Data","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Vector Institute","keywords":"Inference; Deep learning; Transcriptome; Crosstalk; Computational model; Gene regulatory network; Drug discovery","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001460981,0.001033653,0.0008441204,0.0005973737,0.0003504983,0.0003470424,0.002172838,0.001277609,0.00005978409],"category_scores_gemma":[0.0001782662,0.001222306,0.0003014031,0.001060407,0.00009163497,0.00002401751,0.000568792,0.001577558,0.00009048825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288704,"about_ca_system_score_gemma":0.000867687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588874,"about_ca_topic_score_gemma":0.003784193,"domain_scores_codex":[0.9942975,0.001012185,0.001154892,0.002080564,0.0005149705,0.0009399067],"domain_scores_gemma":[0.9962953,0.0002027398,0.0005351935,0.002326008,0.0003146172,0.0003261868],"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":[0.0007135053,0.0007234279,0.0005670242,0.002441606,0.00009682259,0.00002321157,0.006197049,0.009274101,0.9729453,0.00001718447,0.0004514263,0.006549343],"study_design_scores_gemma":[0.002524512,0.0008873109,0.0001189396,0.001506293,0.0005083803,0.000004983356,0.009464577,0.02730898,0.9000762,0.00006536365,0.05562095,0.001913481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6679739,0.02739636,0.2907642,0.000754045,0.001308333,0.00206975,0.0001492815,0.0001442873,0.009439841],"genre_scores_gemma":[0.8545364,0.002051669,0.1089223,0.0004354735,0.0002246307,0.00009788763,0.02759194,0.0003270424,0.005812665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1865626,"threshold_uncertainty_score":0.9990227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04356934166753666,"score_gpt":0.3376433475245073,"score_spread":0.2940740058569706,"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."}}