{"id":"W2006238209","doi":"10.1016/j.gene.2014.06.021","title":"Screening and identification of soybean seed-specific genes by using integrated bioinformatics of digital differential display, microarray, and RNA-seq data","year":2014,"lang":"en","type":"article","venue":"Gene","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Plant Biotechnology Institute","funders":"National Natural Science Foundation of China","keywords":"Biology; Gene; Arabidopsis; Microarray analysis techniques; Gene expression; Microarray; RNA-Seq; Gene expression profiling; Genetics; Computational biology; Transcriptome","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":[0.0001049078,0.00007987225,0.0001167435,0.0000113837,0.00007071071,0.00008024383,0.0001413916,0.00004512013,0.000007159922],"category_scores_gemma":[0.00001706093,0.00003659235,0.00001622197,0.000101189,0.00008255159,0.0001444441,0.0001159254,0.00002806928,3.531914e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002797475,"about_ca_system_score_gemma":0.000001978579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004583408,"about_ca_topic_score_gemma":0.00001001611,"domain_scores_codex":[0.9993662,0.00001591315,0.0002762978,0.0001500109,0.0001040258,0.00008751807],"domain_scores_gemma":[0.9996126,0.0000334488,0.0001839339,0.00008342104,0.00005116084,0.00003541957],"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":[0.000007270388,0.00001509632,0.005766104,0.000008162936,0.00000769032,1.855634e-8,0.00005239039,0.000001647851,0.8849908,0.00001250956,0.00003818491,0.1091001],"study_design_scores_gemma":[0.0003931658,0.000146746,0.166092,0.00004850994,0.00004215541,0.00000700278,0.001090972,0.0628292,0.7677494,0.0001362467,0.001191459,0.0002731288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930416,0.0002832748,0.006081744,0.00003030476,0.00003789589,0.00007429149,0.0004317193,0.000007202087,0.00001200578],"genre_scores_gemma":[0.9979314,0.00008371328,0.0009505527,0.000004605283,0.00004378784,4.994785e-7,0.0009692335,9.810007e-7,0.00001518005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1603259,"threshold_uncertainty_score":0.1492193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03643276668326755,"score_gpt":0.2266676444166223,"score_spread":0.1902348777333548,"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."}}