{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T05:31:41Z","timestamp":1775971901193,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,4,2]],"date-time":"2023-04-02T00:00:00Z","timestamp":1680393600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32070682"],"award-info":[{"award-number":["32070682"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for different types of scRNA-seq data. To assess the performance of STGRNS, we implemented the comparative experiments with some popular methods on extensive benchmark datasets including 21 static and 27 time-series scRNA-seq dataset. All the results show that STGRNS is superior to other comparative methods. In addition, STGRNS was also proved to be more interpretable than \u201cblack box\u201d deep learning methods, which are well-known for the difficulty to explain the predictions clearly.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code and data are available at https:\/\/github.com\/zhanglab-wbgcas\/STGRNS.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad165","type":"journal-article","created":{"date-parts":[[2023,4,2]],"date-time":"2023-04-02T16:11:28Z","timestamp":1680451888000},"source":"Crossref","is-referenced-by-count":54,"title":["STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3870-5212","authenticated-orcid":false,"given":"Jing","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences , Wuhan 430074, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"}]},{"given":"Aidi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences , Wuhan 430074, China"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences , Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8041-0592","authenticated-orcid":false,"given":"Xiujun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences , Wuhan 430074, China"},{"name":"Center of Economic Botany, Core Botanical Gardens, 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