{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T03:52:53Z","timestamp":1773546773906,"version":"3.50.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T00:00:00Z","timestamp":1613088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61403363"],"award-info":[{"award-number":["61403363"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Natural Science of Anhui Provincial Education Department","award":["KJ2020A0018"],"award-info":[{"award-number":["KJ2020A0018"]}]},{"name":"Key project of Anhui Finance and Economics University","award":["ackyb20015"],"award-info":[{"award-number":["ackyb20015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>The GNIPLR tool is freely available at https:\/\/github.com\/zyllluck\/GNIPLR.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab099","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T19:33:45Z","timestamp":1612985625000},"page":"2423-2431","source":"Crossref","is-referenced-by-count":34,"title":["Inference of gene regulatory networks using pseudo-time series data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3517-4011","authenticated-orcid":false,"given":"Yuelei","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics , Bengbu 233030, China"},{"name":"School of Mathematics and Statistics, Shandong University , Weihai, Shandong 264209, China"}]},{"given":"Xiao","family":"Chang","sequence":"additional","affiliation":[{"name":"Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics , Bengbu 233030, China"}]},{"given":"Xiaoping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University , Weihai, Shandong 264209, China"},{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou 310012, China"}]}],"member":"286","published-online":{"date-parts":[[2021,2,12]]},"reference":[{"key":"2023051609133042800_btab099-B1","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1158\/1078-0432.CCR-14-2124","article-title":"MAP2K1 (MEK1) mutations define a distinct subset of lung adenocarcinoma associated with smoking","volume":"21","author":"Arcila","year":"2015","journal-title":"Clin Cancer Res"},{"key":"2023051609133042800_btab099-B2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.cell.2009.03.032","article-title":"Systems biology strikes gold","volume":"137","author":"Camacho","year":"2009","journal-title":"Cell"},{"key":"2023051609133042800_btab099-B3","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"2023051609133042800_btab099-B4","doi-asserted-by":"crossref","first-page":"3305","DOI":"10.1038\/s41467-018-05822-0","article-title":"Linear mapping approximation of gene regulatory networks with stochastic dynamics","volume":"9","author":"Cao","year":"2018","journal-title":"Nat. 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