{"id":"W4306770764","doi":"10.1016/j.bpr.2022.100083","title":"Permissive and nonpermissive channel closings in CFTR revealed by a factor graph inference algorithm","year":2022,"lang":"en","type":"article","venue":"Biophysical Reports","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Defense Advanced Research Projects Agency; Oberlin College and Conservatory; National Sleep Foundation; National Science Foundation","keywords":"Closing (real estate); Cystic fibrosis transmembrane conductance regulator; Channel (broadcasting); Conductance; Transmembrane protein; Inference; Mutant; Patch clamp; Computer science; Algorithm; Graph; Biophysics; Electrophysiology; Cell biology; Biology; Neuroscience; Combinatorics; Mathematics; Artificial intelligence; Computer network; Cystic fibrosis; Theoretical computer science; Genetics; Gene","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":[0.001107997,0.0007123384,0.0007521639,0.001170734,0.0005257376,0.0007565294,0.001151606,0.001080452,0.001801621],"category_scores_gemma":[0.006570537,0.0004713282,0.00106152,0.0005855837,0.0006879822,0.0009414487,0.000567039,0.0009584164,0.0002246262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007813575,"about_ca_system_score_gemma":0.001304264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01342582,"about_ca_topic_score_gemma":0.01455708,"domain_scores_codex":[0.9994407,0.0001760753,0.00003237204,0.0002072871,0.00007774948,0.00006563022],"domain_scores_gemma":[0.9950516,0.004129086,0.0002091516,0.0002393683,0.0002710511,0.00009979917],"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":[0.000198867,0.00006028185,0.002711504,0.00005676728,0.00007134935,0.0001236597,0.00006563783,0.9380281,0.002954599,0.006783701,0.0008472836,0.04809834],"study_design_scores_gemma":[0.000004610092,0.000006924767,0.0001076511,0.000001845667,0.000005355257,0.000009228827,0.000002585268,0.996937,0.0002224271,0.002626264,0.00007358007,0.000002509286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08997859,0.000149716,0.9068481,0.0001863926,0.00001983175,0.00005692884,0.0003306906,0.001432292,0.0009973905],"genre_scores_gemma":[0.600496,0.0001095999,0.3965931,0.0001091005,0.00003320177,0.00007268509,0.001228721,0.000220926,0.001136634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01342582,"threshold_uncertainty_score":0.02669537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007279270803058329,"score_gpt":0.2673321516593538,"score_spread":0.2600528808562954,"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."}}