{"id":"W4390656857","doi":"10.1101/2024.01.04.572289","title":"Compact RNA sensors for increasingly complex functions of multiple inputs","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Bill and Melinda Gates Foundation; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"XNOR gate; Oligonucleotide; Computer science; RNA; Throughput; Computational biology; Computer engineering; DNA; Algorithm; Logic gate; Theoretical computer science; Biology; Gene; Genetics","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":[0.001394409,0.0006929204,0.0006528865,0.0002704717,0.000230174,0.001116556,0.0006705802,0.000855105,0.001616849],"category_scores_gemma":[0.001935283,0.0003515046,0.0003968865,0.0002217864,0.0009890882,0.00138643,0.000912312,0.00126061,0.0005211885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006147678,"about_ca_system_score_gemma":0.0002459635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001650426,"about_ca_topic_score_gemma":0.0003434519,"domain_scores_codex":[0.998909,0.0002074271,0.00006839566,0.000300612,0.0004242313,0.00009039151],"domain_scores_gemma":[0.9990318,0.0005120537,0.0001421227,0.0001409504,0.000121876,0.00005128713],"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":[0.0001193038,0.00004221823,0.0003399301,0.0001104153,0.00001610018,0.0000568696,0.00005650221,0.00760867,0.974866,0.005740538,0.0001646649,0.01087871],"study_design_scores_gemma":[0.00001908092,0.0002785358,0.0002355383,0.00000930485,0.00001056554,0.00008100289,0.00003072697,0.02574109,0.9680412,0.002331545,0.003205024,0.00001631676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.559416,0.001282686,0.430374,0.0007750427,0.0001826323,0.0001781765,0.0003980239,0.001791406,0.005601948],"genre_scores_gemma":[0.771465,0.0005952721,0.2234366,0.0004365289,0.00003072693,0.0002412363,0.000273725,0.0002154974,0.003305431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001616849,"threshold_uncertainty_score":0.007374406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192920886259513,"score_gpt":0.2597006090995247,"score_spread":0.2404085204735734,"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."}}