{"id":"W3149386923","doi":"10.1101/2021.04.01.438014","title":"IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Executable; Computer science; A priori and a posteriori; Gene regulatory network; Trajectory; Computational biology; Data mining; Biology; Gene; Gene expression; Genetics; Programming language","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":[0.0006105242,0.0005258371,0.0004977118,0.00008663735,0.0003077034,0.0001640658,0.0009134992,0.0005201815,0.000006416017],"category_scores_gemma":[0.0004066431,0.0004526293,0.0002166646,0.0001907382,0.0001452661,0.00001821369,0.0004694449,0.0003854706,8.297981e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302882,"about_ca_system_score_gemma":0.0007195974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007378926,"about_ca_topic_score_gemma":0.00001458157,"domain_scores_codex":[0.9975967,0.0001121023,0.0005619092,0.001006598,0.0002929691,0.0004297608],"domain_scores_gemma":[0.9977075,0.0001579555,0.0003908437,0.001344968,0.0002834196,0.000115269],"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":[0.0001797427,0.0001427491,0.003621286,0.0004254893,0.0002464261,0.000003929833,0.00003267527,0.0003874999,0.9948765,0.000005044687,0.00007043,0.000008250687],"study_design_scores_gemma":[0.0008566679,0.0003733085,0.001002466,0.0002435688,0.0002521841,9.024221e-8,0.00001872352,0.002214221,0.9935708,1.81268e-7,0.000992489,0.000475299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864587,0.001283871,0.009036327,0.00002686557,0.001134953,0.0009871053,0.001009253,0.00004902485,0.0000138931],"genre_scores_gemma":[0.9852085,0.00009011044,0.0136945,0.00006767814,0.0006519417,0.0001016938,0.00004758565,0.0001309102,0.000007117263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004658169,"threshold_uncertainty_score":0.9997925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03483058350812452,"score_gpt":0.23465638901311,"score_spread":0.1998258055049855,"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."}}