{"id":"W4399700702","doi":"10.1016/j.csbj.2024.06.012","title":"Target repositioning using multi-layer networks and machine learning: The case of prostate cancer","year":2024,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"UK Research and Innovation","keywords":"Drug repositioning; Drug discovery; Computer science; Computational biology; Biological network; Repurposing; Prostate cancer; Machine learning; Drug target; Interaction network; Artificial intelligence; Gene; Bioinformatics; Cancer; Biology; Drug","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723971,0.0001318407,0.0001510904,0.0001793283,0.0005651427,0.0002755149,0.0001772926,0.00009247841,0.00000525285],"category_scores_gemma":[0.00004589312,0.00008793245,0.00004491883,0.0003439564,0.0002603623,0.0003207268,0.0002411127,0.0006481445,1.530455e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003664371,"about_ca_system_score_gemma":0.00009716064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004563171,"about_ca_topic_score_gemma":0.000004266069,"domain_scores_codex":[0.9989998,0.0001608344,0.0002994769,0.0002571126,0.0001165271,0.0001662211],"domain_scores_gemma":[0.9993114,0.0003012295,0.0001466294,0.00007427565,0.0001132032,0.00005325212],"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":[0.000008480474,0.000003364117,0.001098706,0.00001922322,0.00007359101,0.0004547542,0.0003573938,0.8934426,0.0003411158,0.02917866,0.000008151877,0.07501401],"study_design_scores_gemma":[0.0001752465,0.00004023414,0.002969017,0.00005140869,0.00001537569,0.03302586,0.00004783032,0.921352,0.0002029009,0.04196381,0.00005873438,0.0000976425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4578926,0.006997922,0.5320923,0.002674363,0.0002388253,0.00005578955,0.000005128226,0.00004161711,0.000001528593],"genre_scores_gemma":[0.8525663,0.0001244641,0.1471744,0.00006070976,0.00005406854,0.000001624974,0.000002751471,0.00000625593,0.000009398263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3946737,"threshold_uncertainty_score":0.4346678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688433763150504,"score_gpt":0.3163201845925633,"score_spread":0.2894358469610583,"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."}}