{"id":"W2990174937","doi":"10.1101/853606","title":"A versatile high throughput screening platform for plant metabolic engineering highlights the major role of <i>ABI3</i> in lipid metabolism regulation","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Lipid metabolism and biosynthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada; Commonwealth Scientific and Industrial Research Organisation","keywords":"Metabolic engineering; High-throughput screening; Protoplast; Computational biology; Biology; Mutant; Workflow; Transformation (genetics); Lipid metabolism; Biotechnology; Transgene; Plant biology; Gene; Biomass (ecology); Crop; Genetically modified crops; Biochemistry; Computer science; Botany; Agronomy","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":[0.0006022029,0.0006143932,0.000624617,0.0005640432,0.0004252568,0.001051581,0.000533625,0.0004906464,0.003044822],"category_scores_gemma":[0.0002837202,0.0003339531,0.0005485203,0.0004579358,0.0002827767,0.0004694936,0.0007113141,0.001109672,0.002666292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004401886,"about_ca_system_score_gemma":0.0004374157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009455407,"about_ca_topic_score_gemma":0.001424134,"domain_scores_codex":[0.9995863,0.00004908472,0.0000232588,0.00009081861,0.0002041344,0.00004652072],"domain_scores_gemma":[0.9997984,0.00005791572,0.00003008404,0.00005054805,0.00002838916,0.00003484037],"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":[0.00003248101,0.0000140127,0.0000816033,0.00002574075,0.000005381202,0.00004519323,0.000007706085,0.0001347178,0.9967839,0.000170207,0.0003166156,0.002382604],"study_design_scores_gemma":[0.00001406985,0.000040835,0.001307262,0.000006328789,0.00001304681,0.0002690575,0.00001114146,0.003955869,0.9859192,0.0002925299,0.008150679,0.00001989015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4751333,0.001688595,0.4839632,0.001471693,0.0002667427,0.0004132862,0.01070959,0.01644121,0.009912333],"genre_scores_gemma":[0.6450966,0.001787576,0.3230816,0.0004502149,0.00006622411,0.0004572341,0.01200845,0.002404502,0.01464759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003044822,"threshold_uncertainty_score":0.0101859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007554800195538695,"score_gpt":0.187114108349458,"score_spread":0.1795593081539193,"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."}}