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In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The experiments on the benchmark datasets from DREAM challenge as well as the <jats:italic>E<\/jats:italic>.<jats:italic>c<\/jats:italic><jats:italic>o<\/jats:italic><jats:italic>l<\/jats:italic><jats:italic>i<\/jats:italic> datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03639-7","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T13:38:34Z","timestamp":1594733914000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["PFBNet: a priori-fused boosting method for gene regulatory network inference"],"prefix":"10.1186","volume":"21","author":[{"given":"Dandan","family":"Che","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,14]]},"reference":[{"issue":"7062","key":"3639_CR1","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1038\/nature04209","volume":"437","author":"J-F Rual","year":"2005","unstructured":"Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, et al. 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