{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T22:34:19Z","timestamp":1766788459609,"version":"build-2065373602"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology","doi-asserted-by":"publisher","award":["BT\/PR51150\/NER\/95\/1996\/2023"],"award-info":[{"award-number":["BT\/PR51150\/NER\/95\/1996\/2023"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The inference of gene regulatory networks (GRNs) is critical for understanding the regulatory mechanisms underlying cellular development, functional specialization, and disease progression. Predicting regulatory gene interactions\u2014often framed as a link prediction task\u2014is a foundational step toward modeling cellular behavior. However, GRN inference from gene coexpression data alone is limited by noise, low interpretability, and difficulty in capturing indirect regulatory signals. Additionally, challenges such as data sparsity, nonlinearity, and complex gene interactions hinder accurate network reconstruction. To address these issues, we propose, a novel graph transformer (GT) based framework (GT-GRN) that enhances GRN inference by integrating multimodal gene embeddings. Our method combines three complementary sources of information: (i) autoencoder-based embeddings, which capture high-dimensional gene expression patterns while preserving biological signals; (ii) structural embeddings, derived from previously inferred GRNs and encoded via random walks and a Bidirectional Encoder Representations from Transformers (BERT) based language model to learn global gene representations; (iii) positional encodings, capturing each gene\u2019s role within the network topology . These heterogeneous features are fused and processed using a GT, allowing the joint modeling of both local and global regulatory structures. Experimental results on benchmark datasets show that GT-GRN outperforms existing GRN inference methods in predictive accuracy and robustness. Furthermore, it reconstructs cell-type-specific GRNs with high fidelity and produces gene embeddings that generalize to other tasks such as cell-type annotation.<\/jats:p>","DOI":"10.1093\/bib\/bbaf584","type":"journal-article","created":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T20:21:14Z","timestamp":1762719674000},"source":"Crossref","is-referenced-by-count":1,"title":["<i>GT-GRN<\/i>\n                    : a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4460-044X","authenticated-orcid":false,"given":"Binon","family":"Teji","sequence":"first","affiliation":[{"name":"Network Reconstruction & Analysis (NetRA) Lab , Department of Computer Applications, Sikkim University, 6th Mile, Tadong 737102, Sikkim,","place":["India"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0011-3633","authenticated-orcid":false,"given":"Swarup","family":"Roy","sequence":"additional","affiliation":[{"name":"Network Reconstruction & Analysis (NetRA) Lab , Department of Computer Applications, Sikkim University, 6th Mile, Tadong 737102, Sikkim,","place":["India"]},{"name":"Department of Computer Science and Engineering , Tezpur University, Napaam, Tezpur 784028, Assam,","place":["India"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4412-9848","authenticated-orcid":false,"given":"Dinabandhu","family":"Bhandari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , Heritage Institute of Technology, Kolkata 700107, West Bengal,","place":["India"]}]},{"given":"Jugal","family":"Kalita","sequence":"additional","affiliation":[{"name":"Department of Computer Science , University of Colorado, Colorado Springs, CO, 80918,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"2025110915210831300_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-015-0728-4","article-title":"Netbenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference","volume":"16","author":"Bellot","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"2025110915210831300_ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/978-1-60566-685-3.ch001","article-title":"What are gene regulatory networks?","volume-title":"Handbook of Research on Computational Methodologies in Gene Regulatory Networks","author":"de la Fuente","year":"2010"},{"key":"2025110915210831300_ref3","volume-title":"Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms","author":"Guzzi","year":"2020"},{"key":"2025110915210831300_ref4","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1186\/gb-2005-6-9-404","article-title":"A DNA microarray survey of gene expression in normal human tissues","volume":"6","author":"Shyamsundar","year":"2005","journal-title":"Genome Biol"},{"key":"2025110915210831300_ref5","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.molcel.2015.04.005","article-title":"The technology and biology of single-cell RNA sequencing","volume":"58","author":"Kolodziejczyk","year":"2015","journal-title":"Mol Cell"},{"key":"2025110915210831300_ref6","doi-asserted-by":"crossref","first-page":"19802","DOI":"10.1073\/pnas.1319700110","article-title":"RNA-sequencing from single nuclei","volume":"110","author":"Grindberg","year":"2013","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025110915210831300_ref7","doi-asserted-by":"publisher","first-page":"16329","DOI":"10.1038\/s41598-018-34688-x","article-title":"Autoimpute: autoencoder based imputation of single-cell RNA-seq data","volume":"8","author":"Talwar","year":"2018","journal-title":"Sci Rep"},{"key":"2025110915210831300_ref8","doi-asserted-by":"publisher","volume":"1883","journal-title":"Gene Regulatory Networks. 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