{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:44:50Z","timestamp":1790095490976,"version":"4.0.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0908700"],"award-info":[{"award-number":["2020YFA0908700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["GXXT-2021-039"],"award-info":[{"award-number":["GXXT-2021-039"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202004"],"award-info":[{"award-number":["62202004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322301"],"award-info":[{"award-number":["62322301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62433001"],"award-info":[{"award-number":["62433001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui University Outstanding Youth Research Project","award":["2022AH020010"],"award-info":[{"award-number":["2022AH020010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on graph neural networks, and they can obtain excellent performance in GRN inference by extracting network structure features. However, most of these methods fail to fully exploit the directional characteristics or even ignore them when extracting network structural features. To this end, a novel framework called GAEDGRN is proposed based on gravity-inspired graph autoencoder (GIGAE) to infer potential causal relationships between genes. Among them, GIGAE can help us capture the complex directed network topology in GRN. Additionally, due to the uneven distribution of the latent vectors generated by the graph autoencoder, a random walk-based method is used to regularize the latent vectors learnt by the encoder. Furthermore, considering that some genes in GRN usually have a significant impact on biological functions, GAEDGRN designs a gene importance score calculation method and pays attention to genes with high importance in the process of GRN reconstruction. Experimental results on seven cell types of three GRN types show that GAEDGRN achieves high accuracy and strong robustness. Moreover, a case study on human embryonic stem cells demonstrates that GAEDGRN can help identify important genes.<\/jats:p>","DOI":"10.1093\/bib\/bbaf232","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T07:58:20Z","timestamp":1746518300000},"source":"Crossref","is-referenced-by-count":6,"title":["GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2770-8781","authenticated-orcid":false,"given":"Pi-Jing","family":"Wei","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province , Institute of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601, Anhui,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huai-Wan","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence , Anhui University, 111 Jiulong Road, Hefei 230601, Anhui,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Gao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei 230601, Anhui,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence , Anhui University, 111 Jiulong Road, Hefei 230601, Anhui,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence , Anhui University, 111 Jiulong Road, Hefei 230601, 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