{"id":"W2540287070","doi":"10.1007/978-1-4939-6448-2_6","title":"Characterizing Plexin GTPase Interactions Using Gel Filtration, Surface Plasmon Resonance Spectrometry, and Isothermal Titration Calorimetry","year":2016,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"National Institute of General Medical Sciences","keywords":"Isothermal titration calorimetry; Surface plasmon resonance; Calorimetry; Materials science; Mass spectrometry; Isothermal process; Chemistry; Analytical Chemistry (journal); Nanotechnology; Chromatography; Physical chemistry; Nanoparticle; Thermodynamics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001256278,0.0008317425,0.0008206008,0.0006973399,0.0009020654,0.001043814,0.0006700037,0.0007236831,0.0007479966],"category_scores_gemma":[0.001425608,0.0003042264,0.0004785221,0.0007693516,0.0006125321,0.0007658836,0.0004537786,0.001988045,0.0003581216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000792595,"about_ca_system_score_gemma":0.0004764253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001149743,"about_ca_topic_score_gemma":0.001995273,"domain_scores_codex":[0.9987838,0.0002149394,0.00007603636,0.0002073584,0.0005366933,0.0001811536],"domain_scores_gemma":[0.9993194,0.0003012162,0.00009962324,0.00008208863,0.0001120591,0.00008552866],"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":[0.00009545738,0.0001008781,0.0004186522,0.0000415921,0.00002576287,0.0000169751,0.00004937451,0.0002726607,0.9970305,0.0001419351,0.00009668517,0.001709567],"study_design_scores_gemma":[0.000009683245,0.00006852447,0.003263182,0.000003535341,0.00002260935,0.00008437927,0.00003284659,0.006567302,0.9889147,0.0001534649,0.000859194,0.00002066252],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9722251,0.001046225,0.02469449,0.0002189397,0.00005113454,0.00005058051,0.0004915711,0.0003184394,0.0009035437],"genre_scores_gemma":[0.973177,0.000965579,0.02245241,0.0002264588,0.00002740368,0.0001210309,0.001190213,0.00008517861,0.001754757],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001256278,"threshold_uncertainty_score":0.006643891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735915490553089,"score_gpt":0.3526391695823093,"score_spread":0.3352800146767784,"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."}}