{"id":"W2143047857","doi":"10.1186/1741-7015-10-39","title":"Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery","year":2012,"lang":"en","type":"article","venue":"BMC Medicine","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; University Health Network","funders":"","keywords":"Biomarker discovery; Medicine; Biomarker; Cancer biomarkers; Computational biology; Prostate cancer; Cancer; Bioinformatics; Disease; Identification (biology); Proteomics; Pancreatic cancer; Pathology; Gene; Biology; Internal medicine; 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":[0.001603467,0.000781975,0.0009221736,0.002967687,0.0004611056,0.0009666364,0.0006223455,0.0005612123,0.001411234],"category_scores_gemma":[0.002532756,0.0002410018,0.001086111,0.002914859,0.0003435618,0.0006590915,0.0004415921,0.000664177,0.001116704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005275194,"about_ca_system_score_gemma":0.00108414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003776419,"about_ca_topic_score_gemma":0.0006024342,"domain_scores_codex":[0.9994794,0.0001450707,0.00005772021,0.0001360186,0.0001441214,0.00003771382],"domain_scores_gemma":[0.998508,0.0006641801,0.0003080667,0.0001113474,0.0003086213,0.00009969549],"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":[0.001846269,0.0006734525,0.0665613,0.002138597,0.0004875255,0.001081879,0.0002504395,0.01311443,0.7079686,0.003396721,0.006837356,0.1956434],"study_design_scores_gemma":[0.0002831511,0.001534082,0.1123827,0.0002356011,0.0009460914,0.005846181,0.0004091965,0.351474,0.472325,0.01509568,0.03928093,0.0001874261],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4681329,0.00621774,0.5030548,0.002466665,0.0001573448,0.0007242519,0.009535433,0.006301286,0.00340955],"genre_scores_gemma":[0.503773,0.001694252,0.4819842,0.0004974161,0.00007599412,0.0004652812,0.01047679,0.0002763228,0.0007567786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002967687,"threshold_uncertainty_score":0.008480072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03350617698275909,"score_gpt":0.3095005000655001,"score_spread":0.275994323082741,"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."}}