{"id":"W4392103664","doi":"10.1093/bioinformatics/btae067","title":"Phenotype prediction from single-cell RNA-seq data using attention-based neural networks","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Artificial neural network; Phenotype; Computer science; RNA-Seq; Artificial intelligence; Computational biology; Deep neural networks; Software; Machine learning; Data mining; Pattern recognition (psychology); Biology; Gene; Genetics; Transcriptome; Gene expression","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":[0.0006129263,0.001315784,0.0006881226,0.0007072867,0.0002403377,0.0005247144,0.0008862016,0.0008316721,0.000801815],"category_scores_gemma":[0.001382957,0.0003298887,0.00083377,0.0004439555,0.0003366045,0.0005386999,0.0005048727,0.0009594032,0.0003453171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006326,"about_ca_system_score_gemma":0.0005173403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008244507,"about_ca_topic_score_gemma":0.009541488,"domain_scores_codex":[0.9997839,0.00003406599,0.00001130749,0.0001078996,0.00002928544,0.00003347272],"domain_scores_gemma":[0.9993994,0.0003173329,0.0000714928,0.00004109834,0.0001253907,0.00004536024],"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":[0.0008521589,0.0003879181,0.03729312,0.0002924773,0.0003108311,0.000477418,0.0001287933,0.6279231,0.05565171,0.0009983777,0.00664742,0.2690367],"study_design_scores_gemma":[0.000007432691,0.00003637493,0.001884484,0.000004851508,0.00001874357,0.00002106263,0.00000733862,0.9943297,0.002844032,0.0006626256,0.0001768801,0.000006391897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5175125,0.002573175,0.4654865,0.001010123,0.0002213604,0.0002136046,0.002999095,0.006929841,0.00305385],"genre_scores_gemma":[0.9158055,0.0004104519,0.07578191,0.0006129413,0.000123283,0.0001896737,0.004509474,0.0001049801,0.002461833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008244507,"threshold_uncertainty_score":0.01639307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938044047685513,"score_gpt":0.2431787984064393,"score_spread":0.2037983579295842,"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."}}