{"id":"W4403809881","doi":"10.1093/bib/bbae546","title":"Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"People's Government of Jilin Province; National Natural Science Foundation of China","keywords":"Computer science; Biomarker discovery; Machine learning; Biomarker; Artificial intelligence; Computational biology; Cancer biomarkers; Artificial neural network; Deep learning; Cancer; Biology; Proteomics; Gene","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":[0.0008740608,0.0009989,0.001032383,0.001221598,0.0003276227,0.0008622668,0.001689404,0.001158904,0.001388495],"category_scores_gemma":[0.002194552,0.0005833316,0.001470964,0.0008368914,0.0006349256,0.001031845,0.0009231102,0.001516204,0.0003147442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077129,"about_ca_system_score_gemma":0.0008388326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119677,"about_ca_topic_score_gemma":0.01183774,"domain_scores_codex":[0.9994401,0.000172129,0.00002620876,0.0002031147,0.000103612,0.00005476234],"domain_scores_gemma":[0.9992558,0.0004120519,0.00007240472,0.00005932047,0.0001533481,0.00004709596],"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":[0.00004337949,0.00004413714,0.001536549,0.00007210556,0.00008780599,0.00009587078,0.000045572,0.8921853,0.002035675,0.01243491,0.001836746,0.08958202],"study_design_scores_gemma":[0.000001403929,0.000005703497,0.00007066793,0.000002120955,0.000006155617,0.000006846958,0.000001863963,0.9964857,0.0001497718,0.003058739,0.0002081075,0.000002851005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008146843,0.0006693091,0.9895415,0.0002908909,0.00004734383,0.00002215482,0.0001286178,0.0004092868,0.0007441411],"genre_scores_gemma":[0.570607,0.002036575,0.4188814,0.0005386681,0.0002340488,0.0002597906,0.001429873,0.0001499225,0.005862648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0119677,"threshold_uncertainty_score":0.02379614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009596730354060768,"score_gpt":0.2814474801991158,"score_spread":0.271850749845055,"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."}}