{"id":"W2940275179","doi":"10.2174/1574893614666190410155603","title":"Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies","year":2019,"lang":"en","type":"article","venue":"Current Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Ministry of Education of the People's Republic of China; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Transcriptome; De novo transcriptome assembly; Computational biology; Sequence assembly; DNA sequencing; Computer science; Genome; Hybrid genome assembly; Biology; Reference genome; Gene; Genetics; Gene expression","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":[0.0001496631,0.0001472114,0.0001490899,0.00006323463,0.00006978792,0.00002750654,0.0001647852,0.00008272137,0.000001517904],"category_scores_gemma":[0.00004018429,0.0001282797,0.0001118618,0.00005783384,0.00004455421,0.000001595225,0.00003655218,0.00006222021,0.00001403556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002121271,"about_ca_system_score_gemma":0.00006859866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.39198e-7,"about_ca_topic_score_gemma":3.585474e-7,"domain_scores_codex":[0.9992902,0.000008626875,0.0002425877,0.0001533979,0.0001085236,0.0001966213],"domain_scores_gemma":[0.9995866,0.00003444137,0.00009147184,0.0002122675,0.00005112255,0.0000240913],"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":[0.0004762415,0.0005415596,0.01429008,0.003192079,0.0004594268,6.185288e-7,0.001563487,0.5683555,0.1059037,0.0134038,0.01044681,0.2813666],"study_design_scores_gemma":[0.001859989,0.0009839837,0.001198208,0.00008810779,0.00004195272,0.000004249567,0.001426051,0.9072067,0.02786182,0.001128771,0.05758351,0.0006166811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.901214,0.0008899685,0.09406605,0.0002648816,0.0006297047,0.0009172008,0.0001812173,0.00003572315,0.001801252],"genre_scores_gemma":[0.9827344,0.00003365632,0.01680187,0.00008921012,0.00003894658,0.00004892116,0.0002145054,0.00001219171,0.00002623636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3388512,"threshold_uncertainty_score":0.5231094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04642059490391494,"score_gpt":0.2598677564614635,"score_spread":0.2134471615575486,"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."}}