{"id":"W2944628115","doi":"10.1002/elps.201900028","title":"Ideal‐filter capillary electrophoresis: A highly efficient partitioning method for selection of protein binders from oligonucleotide libraries","year":2019,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capillary electrophoresis; Oligonucleotide; Filter (signal processing); Electrophoresis; Selection (genetic algorithm); Chromatography; Ionic strength; Chemistry; Materials science; Computer science; Biological system; Combinatorial chemistry; Biology; Biochemistry; DNA; Organic chemistry","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":[0.001289064,0.000456367,0.0004268973,0.0008265167,0.0004414723,0.0007604535,0.0009292919,0.0005714778,0.0005791227],"category_scores_gemma":[0.001103364,0.0002929567,0.0001854696,0.0004016954,0.0009254982,0.0008967065,0.0005851695,0.0005282412,0.0004419938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007263595,"about_ca_system_score_gemma":0.0009100084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000988498,"about_ca_topic_score_gemma":0.001308721,"domain_scores_codex":[0.9989104,0.0002223453,0.00004379291,0.0002141461,0.0005033786,0.0001058991],"domain_scores_gemma":[0.9996586,0.0001136704,0.00004879897,0.00002958932,0.0001108014,0.00003859551],"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":[0.0001082429,0.00004025205,0.0004154102,0.0001812705,0.00001467651,0.00007014735,0.00008322121,0.0007706352,0.9679349,0.006381118,0.0007137672,0.02328645],"study_design_scores_gemma":[0.00002110313,0.00009218192,0.0006214626,0.00001159274,0.000009928332,0.0002588856,0.0000207044,0.01388044,0.9718189,0.0009916219,0.01222537,0.00004776715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1550637,0.003563097,0.8350776,0.0005774792,0.0001083922,0.0002918242,0.0002421182,0.0011949,0.003880878],"genre_scores_gemma":[0.4326397,0.003547695,0.5572469,0.000492945,0.00006179499,0.0004513606,0.0004915022,0.0001700406,0.004898091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001289064,"threshold_uncertainty_score":0.006817341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005331306712403541,"score_gpt":0.2387140841072115,"score_spread":0.2333827773948079,"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."}}