{"id":"W4402035240","doi":"10.1093/molbev/msae178","title":"A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development","year":2024,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; State Key Laboratory of Genetic Engineering; Science and Technology Commission of Shanghai Municipality; Institute of Genetics; National Natural Science Foundation of China","keywords":"Biology; Ranking (information retrieval); Core (optical fiber); Gene; Computational biology; Evolutionary biology; Development (topology); Data integration; Genetics; Information retrieval; Data mining; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005450921,0.001140989,0.001547306,0.001539936,0.0007292677,0.002256697,0.003003769,0.001521264,0.001633322],"category_scores_gemma":[0.01299061,0.0009578582,0.001837877,0.001597639,0.001668997,0.001701606,0.002883046,0.002184803,0.0002287848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002316871,"about_ca_system_score_gemma":0.003421136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01326215,"about_ca_topic_score_gemma":0.01060328,"domain_scores_codex":[0.9982117,0.0008396911,0.0001112653,0.0003357641,0.0003686934,0.0001328587],"domain_scores_gemma":[0.9928139,0.00567141,0.0003526362,0.0002778794,0.0006034111,0.0002807594],"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":[0.00003223082,0.00002070428,0.0006990476,0.00006106142,0.00004263653,0.00004611308,0.00003372479,0.9725381,0.000845717,0.01676332,0.0002503972,0.008666883],"study_design_scores_gemma":[0.000002106935,0.000003837198,0.00002887887,0.000001713777,0.000002211694,0.00000278449,0.000002195957,0.996031,0.00008192024,0.003765599,0.00007556179,0.000002222147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007255663,0.000186353,0.9913743,0.0002359244,0.00001715719,0.00005414428,0.0001541471,0.0003493343,0.0003729604],"genre_scores_gemma":[0.261252,0.0003384371,0.7351881,0.0002711701,0.00006469715,0.0006853561,0.0009973233,0.000245878,0.0009571098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01326215,"threshold_uncertainty_score":0.02882755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04620541589736977,"score_gpt":0.3161375722696763,"score_spread":0.2699321563723065,"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."}}