{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:22:00Z","timestamp":1770531720853,"version":"3.49.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102173"],"award-info":[{"award-number":["62102173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["21JR7RA509"],"award-info":[{"award-number":["21JR7RA509"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.<\/jats:p>","DOI":"10.1093\/bib\/bbae542","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T12:01:37Z","timestamp":1729857697000},"source":"Crossref","is-referenced-by-count":2,"title":["Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators"],"prefix":"10.1093","volume":"25","author":[{"given":"Yang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Lanzhou University , Lanzhou 730000 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Lanzhou University , Lanzhou 730000 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