{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:02Z","timestamp":1758672902572,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overall experimental efficiency. Since genes are the foundation of cellular life, building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations. However, current methods fail to fully leverage gene-related information, and solely rely on simple evaluation metrics to construct coarse-grained GRN. More importantly, they ignore functional differences between biotypes, limiting the ability to capture potential gene interactions. In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data, respectively, which serve as the initialization for gene representations. Additionally, we introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes, while capturing implicit gene relationships through graph structure learning (GSL). We propose GRAPE, a heterogeneous graph neural network (HGNN) that leverages gene representations initialized with features from descriptions and sequences, models the distinct roles of genes with different biotypes, and dynamically refines the GRN through GSL. The results on publicly available datasets show that our method achieves state-of-the-art performance. The code for reproducing the results can be seen at the link: https:\/\/github.com\/ChangxiChi\/GRAPE.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/551","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"4949-4957","source":"Crossref","is-referenced-by-count":0,"title":["GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype"],"prefix":"10.24963","author":[{"given":"Changxi","family":"Chi","sequence":"first","affiliation":[{"name":"Zhejiang University"},{"name":"AI Lab, Research Center for Industries of the Future, Westlake University"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"AI Lab, Research Center for Industries of the Future, Westlake University"}]},{"given":"Jingbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University"},{"name":"AI Lab, Research Center for Industries of the Future, Westlake University"}]},{"given":"Jiabei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University"}]},{"given":"Chang","family":"Yu","sequence":"additional","affiliation":[{"name":"AI Lab, Research Center for Industries of the Future, Westlake University"}]},{"given":"Stan Z.","family":"Li","sequence":"additional","affiliation":[{"name":"AI Lab, Research Center for Industries of the Future, Westlake University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:20Z","timestamp":1758627260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/551"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/551","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}