{"id":"W2911600610","doi":"10.1101/531327","title":"MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Simon Fraser University; Deutsche Forschungsgemeinschaft; Terry Fox Foundation; Western Canada Research Grid; Compute Canada","keywords":"Drug response; Representation (politics); Computer science; Precision oncology; Machine learning; Artificial intelligence; Omics; Deep neural networks; Artificial neural network; Computational biology; Drug; Bioinformatics; Precision medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005228443,0.0004680434,0.0003349947,0.0001042435,0.0001518375,0.00008984824,0.0004210597,0.0008085208,0.000002670951],"category_scores_gemma":[0.00009919899,0.0004470833,0.0001727463,0.0001477582,0.0001154086,0.000007926519,0.0002779213,0.0004615763,0.000003236854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008119451,"about_ca_system_score_gemma":0.0001901117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001384205,"about_ca_topic_score_gemma":0.000008415463,"domain_scores_codex":[0.9978976,0.0001684157,0.0003884642,0.001020229,0.0001174811,0.000407763],"domain_scores_gemma":[0.9977903,0.00002822803,0.0003580346,0.001237723,0.0004542837,0.0001314028],"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":[0.0007574981,0.000102119,0.001671568,0.00005843087,0.0001324606,0.000002680066,0.00000388424,0.008630676,0.9881501,0.00009863351,0.0003801367,0.00001178375],"study_design_scores_gemma":[0.001278476,0.0004579521,0.01921794,0.0001091006,0.000193384,1.179612e-7,0.000003571586,0.2213646,0.7489355,0.000002110126,0.007507399,0.0009298952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5131161,0.000402288,0.4843157,0.0001398976,0.0002530766,0.001361937,0.0002894632,0.0001203073,0.000001223587],"genre_scores_gemma":[0.9664363,0.0002315178,0.03127853,0.0003339993,0.000319158,0.001197471,0.00004085982,0.0001346737,0.00002746965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4533202,"threshold_uncertainty_score":0.9997981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00870898139146557,"score_gpt":0.2284956561105074,"score_spread":0.2197866747190418,"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."}}