{"id":"W3023076637","doi":"10.3389/fgene.2020.00377","title":"Prioritizing Cancer Genes Based on an Improved Random Walk Method","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Betweenness centrality; Centrality; Computer science; Random walk; Node (physics); Key (lock); Computational biology; Mathematics; Biology; Statistics","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":[0.0002190756,0.0001996823,0.0002371256,0.00004417412,0.00005811555,0.0000372514,0.0002719625,0.0002109932,0.000007458324],"category_scores_gemma":[0.00002294218,0.0001988537,0.00008329826,0.00011418,0.00004083048,0.000003315931,0.00006586262,0.0001539283,0.000001556513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002426434,"about_ca_system_score_gemma":0.0001120552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000119299,"about_ca_topic_score_gemma":0.00002361073,"domain_scores_codex":[0.9988406,0.00008538787,0.0002965773,0.0003507241,0.0001113712,0.0003153634],"domain_scores_gemma":[0.9993712,0.000009135128,0.00008977628,0.000317665,0.00004220499,0.0001699886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001709934,0.0001450946,0.01668306,0.0001504299,0.0001529711,0.00001098457,0.0007047125,0.09086314,0.2598316,0.00002019888,0.01788868,0.6118392],"study_design_scores_gemma":[0.00284878,0.0005175452,0.0005664462,0.00001747017,0.00003199944,0.000001122891,0.000180543,0.9008796,0.03308279,0.00009737767,0.06138594,0.0003903697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0858339,0.00808142,0.9023298,0.0009356001,0.001281554,0.0006733571,0.00007571199,0.00003029612,0.0007583505],"genre_scores_gemma":[0.4959794,0.00185241,0.4937507,0.006811371,0.001171685,0.00007065346,0.0001784939,0.00007719879,0.0001081596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8100165,"threshold_uncertainty_score":0.8109018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022450323122549,"score_gpt":0.2600367944392117,"score_spread":0.2498122912079863,"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."}}