{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841732,0.001066048,0.0009379761,0.001628354,0.0004753857,0.001523462,0.0007485592,0.000594016,0.001355149],"category_scores_gemma":[0.003181532,0.0005124647,0.001108106,0.001066052,0.0002548654,0.0006561645,0.0007006123,0.0004968115,0.0006221493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005660785,"about_ca_system_score_gemma":0.001682842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001926393,"about_ca_topic_score_gemma":0.002843899,"domain_scores_codex":[0.9992365,0.0001919543,0.00008463053,0.0002285042,0.0002040873,0.00005450907],"domain_scores_gemma":[0.9987952,0.0007122824,0.000146625,0.00008293104,0.0002034951,0.00005951883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002895525,0.0009773665,0.04084668,0.00211069,0.000845262,0.00228271,0.0004413865,0.1344463,0.6401898,0.004037137,0.00747623,0.1634509],"study_design_scores_gemma":[0.0001696061,0.0004808227,0.01302869,0.00005612502,0.0004461659,0.0008381891,0.0001866048,0.6394777,0.3239542,0.005375392,0.01589378,0.00009267087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4166722,0.001258534,0.5570384,0.0005457859,0.0000836188,0.000716402,0.01002486,0.01155812,0.002102069],"genre_scores_gemma":[0.4184272,0.0008229049,0.5551082,0.000338801,0.000026956,0.0004540267,0.02298442,0.0006630347,0.001174396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001926393,"threshold_uncertainty_score":0.009740174,"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."}}