{"id":"W2973158250","doi":"10.1145/3307339.3343480","title":"A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability","year":2019,"lang":"en","type":"article","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Breast cancer; Survivability; Support vector machine; Machine learning; Computer science; Biological network; Classifier (UML); Artificial intelligence; Predictive power; Cancer; Interaction network; Computational biology; Gene; Bioinformatics; Data mining; Biology; Medicine; Internal medicine; Computer network","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":[0.0009030427,0.0008877693,0.0008031536,0.003584507,0.0003574609,0.0007884991,0.0007276252,0.0006728221,0.0008952002],"category_scores_gemma":[0.002845915,0.0002452623,0.0007958576,0.00202716,0.0002716496,0.00112044,0.0005324848,0.0007186527,0.0002713889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007854758,"about_ca_system_score_gemma":0.0005743176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002983062,"about_ca_topic_score_gemma":0.0037551,"domain_scores_codex":[0.9995748,0.0001381377,0.00003366804,0.0001207957,0.00009909052,0.00003336238],"domain_scores_gemma":[0.9990531,0.0005699393,0.0001167316,0.00005746272,0.0001715477,0.00003129839],"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":[0.0002003807,0.0002553131,0.0226719,0.0002959979,0.0004919393,0.0002437516,0.00009351721,0.6589115,0.01089578,0.01115883,0.001735711,0.2930454],"study_design_scores_gemma":[0.000003932787,0.00004169581,0.001671026,0.0000121064,0.00004530304,0.00004085582,0.00001345251,0.987788,0.0009891189,0.008666042,0.0007188777,0.000009502873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04760416,0.001684377,0.9468392,0.0004421996,0.00004868428,0.0001315103,0.000947091,0.0006832146,0.001619487],"genre_scores_gemma":[0.6194729,0.001402875,0.3751008,0.000148708,0.0001127301,0.0003352067,0.001765135,0.0000493663,0.001612382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003584507,"threshold_uncertainty_score":0.005931377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437667432828139,"score_gpt":0.2489297829816814,"score_spread":0.2345531086534001,"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."}}