{"id":"W2976312226","doi":"10.1038/s41598-019-50224-x","title":"Network-based method for drug target discovery at the isoform level","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Education Department of Shaanxi Province; China Scholarship Council","keywords":"Gene isoform; Computational biology; In silico; Alternative splicing; Biology; Gene; Drug discovery; Bioinformatics; Genetics","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":[0.0008966198,0.001452561,0.001160741,0.003420605,0.0004625247,0.0009930766,0.001078314,0.0008232453,0.005434608],"category_scores_gemma":[0.002731481,0.0004803936,0.001605719,0.002109008,0.0002761803,0.0007532563,0.0006927084,0.001228474,0.001004195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009413935,"about_ca_system_score_gemma":0.001430347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005553887,"about_ca_topic_score_gemma":0.007001033,"domain_scores_codex":[0.9995654,0.0001206574,0.00003535602,0.0001202675,0.0001304541,0.00002771944],"domain_scores_gemma":[0.9991844,0.0005015178,0.00009772816,0.00006571873,0.0001201966,0.00003041457],"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":[0.0004658883,0.0002760433,0.004568938,0.001204871,0.0009488901,0.0003712726,0.00009624098,0.5784726,0.01366132,0.01979761,0.01519221,0.3649442],"study_design_scores_gemma":[0.00002687074,0.00004420724,0.0004931131,0.00002203109,0.00007270886,0.00007779655,0.000015222,0.980113,0.001253195,0.01271193,0.005157528,0.00001246338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008489967,0.0009089918,0.9799331,0.0003393756,0.00007483629,0.0003605598,0.003594484,0.0043029,0.001995646],"genre_scores_gemma":[0.1399511,0.001503868,0.8437663,0.0002125018,0.00008097303,0.001124072,0.009965342,0.0003580939,0.003037859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005553887,"threshold_uncertainty_score":0.01818055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999852964405034,"score_gpt":0.3067058601472578,"score_spread":0.2767073305032075,"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."}}