{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:39:00Z","timestamp":1762353540738,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of General Medical Sciences of the National Institutes of Health","award":["P2O GM103424-21"],"award-info":[{"award-number":["P2O GM103424-21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer\u2019s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks\u2019 intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman\u2019s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into \u2018Positive\u2019 and \u2018Negative\u2019 categories. \u2018Positive\u2019 subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. \u2018Negative\u2019 subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease.<\/jats:p>","DOI":"10.3390\/make6030089","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T16:14:58Z","timestamp":1722615298000},"page":"1818-1839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Graph Representation Convolution: Effective Inferring Gene Regulatory Network Using Graph Convolution Network with Self-Attention Graph Pooling Layer"],"prefix":"10.3390","volume":"6","author":[{"given":"Duaa Mohammad","family":"Alawad","sequence":"first","affiliation":[{"name":"Computer Science, University of New Orleans, 2000 Lakeshore Drive, Math 308, New Orleans, LA 70148, USA"}]},{"given":"Ataur","family":"Katebi","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Northeastern University, Boston, MA 02115, USA"},{"name":"Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-2194","authenticated-orcid":false,"given":"Md Tamjidul","family":"Hoque","sequence":"additional","affiliation":[{"name":"Computer Science, University of New Orleans, 2000 Lakeshore Drive, Math 308, New Orleans, LA 70148, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for robust gene network inference","volume":"9","author":"Marbach","year":"2012","journal-title":"Nat. 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