{"doi":"10.12688/f1000research.26128.2","title":"Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty","abstract":"<ns3:p> Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with <ns3:italic>L1 </ns3:italic> (lasso), <ns3:italic>L2 </ns3:italic> (ridge), or elastic net penalty, which spans the range of <ns3:italic>L1 </ns3:italic> to <ns3:italic>L2 </ns3:italic> penalty. However, for high dimensional gene expression data, a penalty that spans the range of <ns3:italic>L0 </ns3:italic> and <ns3:italic>L1 </ns3:italic> penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package <ns3:italic>space-log</ns3:italic> . We show that the <ns3:italic>space-log</ns3:italic> is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs. <ns3:italic>Space-log</ns3:italic> is open source and available at GitHub, https://github.com/wuqian77/SpaceLog </ns3:p>","journal":"F1000Research","year":2022,"id":295322,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9635,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":286050,"name":"Wei Sun","orcid":"0000-0002-6350-1107","position":1,"is_corresponding":false},{"id":218620,"name":"Li Hsu","orcid":"0000-0001-8168-4712","position":2,"is_corresponding":false},{"id":537836,"name":"Qian Wu","orcid":"0000-0002-2292-4816","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T00:31:08.861377Z","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":[]}