{"id":"W3198096727","doi":"10.1101/2021.09.01.456741","title":"Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"HeLa; Limonene; Metabolite; Cancer; Carcinogen; Computational biology; Chemistry; Detection limit; Reporter gene; Cancer research; Biochemistry; Gene; Biology; Chromatography; Gene expression; Cell; 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":[0.0002979031,0.0003381201,0.0002314012,0.0002064101,0.00009480554,0.0004124648,0.000386603,0.000557113,0.0006075256],"category_scores_gemma":[0.0004137703,0.0001865597,0.0003062924,0.0001696692,0.0002691417,0.0003463386,0.0003542107,0.0004508484,0.0003980146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000449269,"about_ca_system_score_gemma":0.0002280214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000302507,"about_ca_topic_score_gemma":0.0003535414,"domain_scores_codex":[0.9998201,0.00003035988,0.00001625547,0.00005438301,0.00005581471,0.0000232295],"domain_scores_gemma":[0.9997694,0.00006008397,0.00008687562,0.00002382918,0.00003564005,0.00002414587],"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":[0.00001658171,0.000008391681,0.0001056506,0.00003905154,0.00000390871,0.00002103952,0.000006020845,0.0002992731,0.9981268,0.0002119448,0.00002981184,0.001131579],"study_design_scores_gemma":[0.000006871497,0.00007930726,0.0001824172,0.000003866788,0.000006367751,0.0000442932,0.000007002723,0.002607441,0.9949638,0.00006108883,0.002031433,0.000006166306],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8337536,0.002222057,0.1589089,0.000683729,0.0002093545,0.0002376955,0.001125101,0.0008423013,0.002017203],"genre_scores_gemma":[0.9030994,0.001281096,0.09120823,0.0002136408,0.00002722198,0.000145366,0.0007368959,0.0001059087,0.003182115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006075256,"threshold_uncertainty_score":0.003259718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008358398509219432,"score_gpt":0.2009944444000741,"score_spread":0.1926360458908547,"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."}}