{"id":"W4388585137","doi":"10.59275/j.melba.2023-3d9d","title":"Towards Early Prediction of Human iPSC Reprogramming Success","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Pluripotent Stem Cells Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Induced pluripotent stem cell; Reprogramming; Computer science; Artificial intelligence; Source code; Segmentation; Regenerative medicine; Computational biology; Machine learning; Biology; Stem cell; Embryonic stem cell; Cell; Cell biology; Operating system; 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":[],"consensus_categories":[],"category_scores_codex":[0.003179433,0.0001036012,0.0001827493,0.000191552,0.000194125,0.00002553031,0.000418646,0.00006141413,0.00001124889],"category_scores_gemma":[0.0006289852,0.00007065845,0.0001576208,0.000287337,0.0002357484,0.00001048928,0.0001915758,0.0003731248,0.000002300419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000161385,"about_ca_system_score_gemma":0.00006199181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009131202,"about_ca_topic_score_gemma":0.000001758064,"domain_scores_codex":[0.9984369,0.0002058618,0.0004522231,0.0001365308,0.000478135,0.0002903189],"domain_scores_gemma":[0.9990916,0.0000768173,0.000334903,0.0001750821,0.0002122871,0.0001092872],"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":[0.0002298361,0.0000642045,0.03139101,0.0001064585,0.0001281905,0.00001372949,0.0002312,0.0002770948,0.9240032,0.00001144918,0.001319752,0.04222381],"study_design_scores_gemma":[0.01175817,0.01045916,0.2155769,0.001024809,0.0006459801,0.001222677,0.001549867,0.1037249,0.4449616,0.001226642,0.2069178,0.0009315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887137,0.0006205011,0.008845561,0.001332548,0.0001823296,0.0001424454,0.00001133879,0.00001933578,0.000132263],"genre_scores_gemma":[0.9986411,0.0001673857,0.000313385,0.00002227441,0.0003900012,0.000003468342,0.00007275737,0.00002529688,0.0003643478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4790416,"threshold_uncertainty_score":0.2881368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815780357043608,"score_gpt":0.3210128194884764,"score_spread":0.3028550159180403,"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."}}