{"doi":"10.1145/3538950.3538955","title":"Applications of Elastic Net technology in survival analysis of high-dimensional data","abstract":null,"journal":"2022 4th International Conference on Big Data Engineering","year":2022,"id":46555,"datarank":0.12791619106778393,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.02394411398379213,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.02394411398379213,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"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":215770,"name":"Yuxue Hu","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Applications of Elastic Net technology in survival analysis of high-dimensional data","abstract":"With the rapid development of big data technology, gene recognition, protein structure detection and other technologies, the characteristics of survival data gradually tend to be small sample size, high dimension and strong correlation. Elastic Net technology improves LASSO method and makes up for the shortcomings of LASSO method in dealing with the correlated variables. In this paper, LASSO method and Elastic Net technology are combined with Cox model, and the effects of the two methods are compared through data simulation and case analysis. The results show that the Elastic Net technology has more advantages in dealing with correlated and high-dimensional survival data.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31712217","pmcid":null,"openalex_id":"https://openalex.org/W4285814086","authors":[],"funders":[],"total_grants":0,"fwci":0.1326,"citation_percentile":0.5171616,"influential_citations":0,"citation_trend":[{"year":2025,"count":1}],"oa_status":"closed","license":"https://www.acm.org/publications/policies/copyright_policy#Background","oa_locations":[{"url":"https://dl.acm.org/doi/10.1145/3538950.3538955","host_type":"publisher"},{"url":"https://dl.acm.org/doi/pdf/10.1145/3538950.3538955","host_type":"publisher"},{"url":"https://doi.org/10.1145/3538950.3538955","host_type":""}],"fields_of_study":["Neural Networks and Applications","Statistical Methods and Inference","Gene expression and cancer classification","Computer Science","Mathematics"],"mesh_terms":[],"keywords":["Elastic net regularization","Lasso (programming language)","Big data","Computer science","Net (polyhedron)","Dimension (graph theory)","Sample (material)","Data mining","Sample size determination","High dimensional","Artificial intelligence","Statistics","Mathematics","Feature selection"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Industry, innovation and infrastructure"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-10T23:10:24.510271Z","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":[]}