{"id":"W2327174080","doi":"10.1021/acs.jctc.5b00209","title":"Computational Lipidomics with <i>insane</i>: A Versatile Tool for Generating Custom Membranes for Molecular Simulations","year":2015,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1212,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Deutsche Forschungsgemeinschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Lipidomics; Computer science; Nanotechnology; Membrane; Data science; Computational biology; Chemistry; Materials science; Bioinformatics; Biology; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006136221,0.001143116,0.0008927831,0.0003897841,0.0008602545,0.0008297035,0.002183283,0.001082438,0.01026077],"category_scores_gemma":[0.001348881,0.0007771897,0.0009611406,0.0005743987,0.0004940265,0.0009389109,0.001337671,0.002607789,0.001719457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007369157,"about_ca_system_score_gemma":0.001227551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00305378,"about_ca_topic_score_gemma":0.00359094,"domain_scores_codex":[0.9998341,0.00004274872,0.00000831564,0.0000278848,0.00006067347,0.00002622419],"domain_scores_gemma":[0.9996009,0.000207399,0.00002523287,0.00006913462,0.00005323491,0.00004396299],"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":[0.0006951268,0.0003134642,0.003603188,0.001183296,0.0005724553,0.0006318972,0.0007229867,0.7089787,0.05149941,0.1049642,0.05840391,0.06843135],"study_design_scores_gemma":[0.0001121671,0.00005160145,0.0004222299,0.00003503627,0.00003253907,0.00008825312,0.00003832946,0.938574,0.01625656,0.01293887,0.0314079,0.0000425248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.1280765,0.0006779389,0.7814555,0.001185767,0.0003175479,0.0004506482,0.01214416,0.03926326,0.03642868],"genre_scores_gemma":[0.2552262,0.001022884,0.7056273,0.0004685347,0.00008558804,0.002498363,0.01288226,0.01274512,0.009443834],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01026077,"threshold_uncertainty_score":0.03432566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331562127467239,"score_gpt":0.2714022487137325,"score_spread":0.2580866274390601,"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."}}