{"id":"W4414910998","doi":"10.1073/pnas.2501324122","title":"Dynamic sensor selection for biomarker discovery","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Air Force Research Laboratory; Air Force Office of Scientific Research; National Institute of General Medical Sciences; National Science Foundation","keywords":"Observability; Biomarker discovery; Biomarker; Selection (genetic algorithm); Generality","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":[0.001906068,0.0007857067,0.0007153592,0.001016584,0.0003827939,0.0008877107,0.0009086009,0.0007222776,0.001085819],"category_scores_gemma":[0.006534391,0.000338339,0.0006146526,0.0009018265,0.0009859054,0.001297166,0.001083081,0.0009126698,0.0002433414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009559729,"about_ca_system_score_gemma":0.000754845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025332,"about_ca_topic_score_gemma":0.001047352,"domain_scores_codex":[0.9988514,0.0004207643,0.00005119032,0.0003247451,0.0002750452,0.00007678862],"domain_scores_gemma":[0.9982065,0.001117095,0.0002332026,0.0001785828,0.0002028353,0.00006169944],"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":[0.0005113187,0.0001920415,0.006013928,0.0003145386,0.0001930331,0.0003835203,0.000176394,0.5216406,0.1209978,0.07583986,0.002550089,0.2711869],"study_design_scores_gemma":[0.00001933067,0.0001046212,0.0009414675,0.00001459629,0.00002102541,0.00008922288,0.0000265123,0.9306443,0.02308492,0.0423885,0.002637426,0.00002816775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01448795,0.0004311321,0.9832523,0.0002714251,0.00003082995,0.00004116558,0.00007666475,0.0003109281,0.001097646],"genre_scores_gemma":[0.7564627,0.0007282211,0.2401311,0.0002684501,0.00009579021,0.0002235414,0.0003374812,0.00008970296,0.00166307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001906068,"threshold_uncertainty_score":0.01008034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589405913361733,"score_gpt":0.2995811773455037,"score_spread":0.2836871182118864,"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."}}