{"id":"W4311924798","doi":"10.1101/2022.12.16.520772","title":"Fatecode: Cell fate regulator prediction using classification autoencoder perturbation","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; University Health Network; University of Toronto; Sinai Health System; University of Waterloo","funders":"","keywords":"Cell fate determination; Reprogramming; Regulator; Computer science; Gene regulatory network; Cell type; Cell; Autoencoder; Computational biology; Biology; Transcriptome; Artificial intelligence; Transcription factor; Gene; Gene expression; Deep learning; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004361894,0.0005156533,0.000353933,0.0001832195,0.0003514701,0.0001601429,0.0004832219,0.0007452118,0.0000739126],"category_scores_gemma":[0.00006630055,0.0006101283,0.0002204748,0.0002524921,0.00009342428,0.00001865187,0.0003341933,0.0006105058,0.00000938118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199247,"about_ca_system_score_gemma":0.0005758559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004296799,"about_ca_topic_score_gemma":0.000001956277,"domain_scores_codex":[0.9971601,0.0002001509,0.0005886475,0.001211157,0.0003939648,0.0004460169],"domain_scores_gemma":[0.9977897,0.00001159431,0.000469015,0.001208724,0.0003419071,0.0001789999],"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":[0.00006735433,0.0001669878,0.005347135,0.0001887237,0.00007824202,0.000003780912,0.00001104818,0.00389566,0.9898764,0.00006008974,0.0003021088,0.000002391215],"study_design_scores_gemma":[0.0008482075,0.0001798024,0.03612756,0.00009696226,0.0002209883,5.466184e-8,0.00001143103,0.05641419,0.8882744,0.000004638379,0.01678829,0.001033511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9644706,0.001005945,0.03073602,0.00007627984,0.002263153,0.000706018,0.0004977736,0.0002022858,0.00004192983],"genre_scores_gemma":[0.9917758,0.0003624071,0.006479697,0.000149302,0.0008008786,0.0001619722,0.00002380527,0.0001708119,0.00007533524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1016021,"threshold_uncertainty_score":0.999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304912764151957,"score_gpt":0.2221716713531693,"score_spread":0.1991225437116498,"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."}}