{"id":"W4200608484","doi":"10.1002/pmic.202100230","title":"In‐silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases","year":2021,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"In silico; Computational biology; Cancer; Proteomics; Selection (genetic algorithm); Workflow; Cancer biomarkers; Proteome; Bioinformatics; Breast cancer; Biology; Pipeline (software); Database; Gene; Computer science; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104621,0.0002057665,0.0002746367,0.00005323832,0.00009674067,0.00002763421,0.0001469378,0.0001183318,0.0001588871],"category_scores_gemma":[0.00009057925,0.0002215728,0.00004369947,0.0002247601,0.0000803752,0.000174581,0.0001509048,0.0003881789,0.000001294271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054393,"about_ca_system_score_gemma":0.0001840554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006197985,"about_ca_topic_score_gemma":0.0003054507,"domain_scores_codex":[0.9987143,0.00001901993,0.000433828,0.0004655764,0.0001082419,0.0002590693],"domain_scores_gemma":[0.9992577,0.00003094659,0.0002573824,0.0002892929,0.0001019522,0.00006268631],"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":[0.00003157411,0.0001417082,0.01313315,0.0002263009,0.0000231812,0.000001950503,0.00007183479,0.00002200274,0.9837767,0.001078978,0.00003719323,0.001455362],"study_design_scores_gemma":[0.0005708271,0.00001944504,0.0001507595,0.0001631664,0.00002468187,0.00002734316,0.00005790146,0.002202055,0.9946768,0.001140301,0.000740917,0.0002257877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875935,0.000377162,0.01027322,0.0002148105,0.0000119716,0.0008592587,0.0002859755,0.00007343914,0.0003106192],"genre_scores_gemma":[0.5969715,0.0008978716,0.3979516,0.00005345695,0.00009011485,0.003332563,0.00009525601,0.00006154256,0.0005460996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.390622,"threshold_uncertainty_score":0.9035477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173351573572787,"score_gpt":0.2810468637409506,"score_spread":0.2693133480052227,"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."}}