{"id":"W2955502047","doi":"10.1093/bioinformatics/btz318","title":"MOLI: multi-omics late integration with deep neural networks for drug response prediction","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":407,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Canada Foundation for Innovation","keywords":"Drug response; Computer science; Representation (politics); Machine learning; Artificial intelligence; Omics; Precision oncology; Artificial neural network; Computational biology; Data mining; Drug; Precision medicine; Bioinformatics; Biology","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":[0.001739378,0.002151058,0.001500639,0.001420994,0.0003407212,0.001088042,0.001988208,0.001601907,0.002113415],"category_scores_gemma":[0.003012094,0.0005651776,0.001466643,0.001239714,0.0003870634,0.001129382,0.00156821,0.00254249,0.0007691149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001722425,"about_ca_system_score_gemma":0.001431206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006165219,"about_ca_topic_score_gemma":0.007571811,"domain_scores_codex":[0.999433,0.000142246,0.00003323162,0.0001440732,0.0001537544,0.00009368087],"domain_scores_gemma":[0.9993058,0.0003177108,0.0001189667,0.0000732386,0.0001291748,0.00005503238],"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":[0.0007902061,0.0006774492,0.01439816,0.0006097226,0.0008572573,0.0003581514,0.0001129249,0.5752792,0.01511381,0.004018153,0.0210484,0.3667366],"study_design_scores_gemma":[0.00001442192,0.00006682864,0.0005470891,0.00001697918,0.00003852174,0.00002724667,0.00000668726,0.9940704,0.00217333,0.002161146,0.0008648022,0.00001257087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1085525,0.008728731,0.8443263,0.003481431,0.0003549055,0.0004281357,0.006199761,0.02209473,0.005833456],"genre_scores_gemma":[0.6858149,0.001661355,0.2897608,0.002354423,0.0003307235,0.0007332042,0.01255741,0.0004341541,0.006353132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006165219,"threshold_uncertainty_score":0.01249713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007039244457505459,"score_gpt":0.2143085593870241,"score_spread":0.2072693149295187,"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."}}