{"id":"W2789567894","doi":"10.1101/259002","title":"Candidate SNP analyses integrated with mRNA expression and hormone levels reveal influence on mammographic density and breast cancer risk","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Single-nucleotide polymorphism; SNP; Breast cancer; Candidate gene; Biology; Gene; Genotype; Gene expression; Bioinformatics; Genetic association; Genetics; Oncology; Cancer; Medicine","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":[0.0009015675,0.0002645116,0.0003049716,0.0007701137,0.0002059077,0.0004188592,0.0002398444,0.0003089952,0.001588872],"category_scores_gemma":[0.001425065,0.0001380254,0.0005956324,0.0009779368,0.0002870913,0.00009482254,0.0002294258,0.0002632669,0.0002241905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001845386,"about_ca_system_score_gemma":0.0002910363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003080224,"about_ca_topic_score_gemma":0.003427546,"domain_scores_codex":[0.9995529,0.0001400585,0.00003104874,0.0001368423,0.00008524713,0.00005391246],"domain_scores_gemma":[0.9991606,0.0004740172,0.000209178,0.00005288687,0.00004060976,0.00006270979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007566506,0.00004890041,0.9549781,0.00007068209,0.0004801406,0.0003214037,0.0001261515,0.0006772359,0.03532995,0.00008239131,0.0001389565,0.006989457],"study_design_scores_gemma":[0.00001550992,0.0001354344,0.9951453,0.000007892486,0.0002307528,0.0002868247,0.00005957223,0.0009823383,0.002780451,0.00008307615,0.0002677175,0.000005060202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977664,0.000399697,0.001084006,0.00003181355,0.00000601075,0.000006718829,0.000506644,0.00001480475,0.0001838694],"genre_scores_gemma":[0.9978431,0.0001268352,0.001385326,0.00002114992,0.000005445173,0.00001366038,0.0004040698,0.000005745536,0.0001946225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003080224,"threshold_uncertainty_score":0.006124556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843534684581842,"score_gpt":0.251228294581503,"score_spread":0.2327929477356845,"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."}}