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oai:figshare.com:article/34073624 2026-10-05T17:49:56Z category_12 category_13 category_873 category_734 category_931 category_64 portal_5 item_type_3 month_year_10_2026

<p>Comparison table of performance of different cancer prediction genes.</p> Yue Yuan (113839) Yu Liu (6938) Jingchun Fan (9241232) Zhen Ren (2258311) Cell Biology Genetics Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer virtual knockout techniques produced predicted probabilities like growth factor lethal malignancies worldwide enhance node features corresponding importance scores provided interpretable analyses fibronectin 1 (< colorectal &# 8220 cancer ” transition cancer &# 8221 differentially expressed genes graph level representations div >< p colorectal cancer groups key genes involved eight screened genes colorectal “ adenoma level representations colorectal cancer key genes enrichment analyses 33 (< genes affect stc2 </ sfrp1 </ sample classification precise identification overall survival nox4 </ named chebts multifeature learning mmp1 </ kyoto encyclopedia igfbp7 </ gse31905 datasets great significance geo database gene ontology following activation fn1 </ feature encoding extracellular mechanisms degs ). colorectal adenocarcinoma cldn2 </ biological processes <p>Comparison table of performance of different cancer prediction genes.</p> 2026-10-05T17:49:37Z Dataset Dataset 10.1371/journal.pone.0359785.t003 https://figshare.com/articles/dataset/_p_Comparison_table_of_performance_of_different_cancer_prediction_genes_p_/34073624 CC BY 4.0