{"doi":"10.1109/tcbbio.2025.3573742","title":"NetWalkRank: Cancer Driver Gene Prioritization in Multiplex Gene Regulatory Networks by a Random Walk Approach","abstract":null,"journal":"IEEE Transactions on Computational Biology and Bioinformatics","year":2025,"id":645862,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":320460,"name":"Lingyu Li","orcid":"0000-0002-4559-2711","position":1,"is_corresponding":false},{"id":1681891,"name":"Wai-Ki Ching","orcid":"0000-0002-5785-3210","position":2,"is_corresponding":false},{"id":880997,"name":"Zhi-Ping Liu","orcid":"0000-0002-2437-3536","position":3,"is_corresponding":false},{"id":226012,"name":"Fatemeh Keikha","orcid":"0000-0003-1760-6394","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"NetWalkRank: Cancer Driver Gene Prioritization in Multiplex Gene Regulatory Networks by a Random Walk Approach","abstract":"Finding and prioritizing cancer driver genes (CDGs) that disrupt normal cell functionality and contribute to cancer occurrence and development is a significant challenge in oncology. Integrating multiple information pertaining to the characteristics of each gene at different stages of the disease and incorporating multiple steps as individual layers in the model provides a more comprehensive understanding of each node or gene. Thus, it is reasonable to organize them into multiplex gene regulatory networks (GRNs). In this work, we present a network-based framework called NetWalkRank, for prioritizing CDGs in the multiplex GRNs with gene expression profiling data. The framework applies the concept of network propagation to calculate the relative impact of each gene in spreading abnormality throughout the multiplex GRNs. It was employed to give priority to the driver genes of hepatocellular carcinoma (HCC) in humans. The performance of NetWalkRank was demonstrated through the ranks and classifications assigned to the known CDGs, which validated its effectiveness. To showcase the predictive capabilities of our proposed framework, we trained a random forest model that utilizes the obtained scores to accurately predict CDGs. We compared the advantage and efficiency of our method with other well-known driver gene ranking methods through numerical experiments. The findings show that the usage of GRNs across various steps of multiplex networks in prioritizing and predicting CDGs is significant, as demonstrated by the efficiency and effectiveness of NetWalkRank.","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40811206","pmcid":null,"openalex_id":"https://openalex.org/W4410738144","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2020YFA0712402","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"92374107","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62373216","title":null},{"funder_name":"Hong Kong RGC GRF","grant_id":"17309522","title":null},{"funder_name":"Shandong Provincial Key Research and Development Program","grant_id":"2023CXGC010509","title":null}],"total_grants":5,"fwci":0.8148,"citation_percentile":0.69906633,"influential_citations":0,"citation_trend":[{"year":2025,"count":1},{"year":2026,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx8/10723156/11121392/11015713.pdf?arnumber=11015713","host_type":"publisher"},{"url":"https://doi.org/10.1109/tcbbio.2025.3573742","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40811206","host_type":"repository"}],"fields_of_study":["Gene Regulatory Network Analysis","CRISPR and Genetic Engineering","Cancer Genomics and Diagnostics"],"mesh_terms":[],"keywords":["Prioritization","Multiplex","Gene","Computational biology","Genetics","Cancer","Gene regulatory network","Biology","Computer science","Gene expression","Engineering"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T10:26:28.185213Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}