{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:58:13Z","timestamp":1793278693945,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Prioritizing genes associated with agronomic traits remains challenging because relevant evidence is distributed across heterogeneous biological resources and curated gene-trait associations are incomplete.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We formulate multi-trait crop gene prioritization as gene-trait link prediction on a rice-centered heterogeneous knowledge graph, where each candidate pair represents a graph-supported ranking hypothesis rather than an independent biological validation. We introduce a cross-residual knowledge-graph learning framework that keeps structural and relation-aware propagation in separate branches while allowing layer-wise residual exchange between them. This design lets broad topology and typed biological context refine each other during message passing, and residual filtering or transformation controls weak cross-branch corrections.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Under random and cold-gene evaluation protocols, Cross + JK variants provide consistent information gain within strict dual-branch architectural controls, remain near the top across the broader benchmark, and exhibit small seed-to-seed standard deviations on random-split ranking metrics. The default cold-gene split shows that feature-only MLP is a strong prior-driven baseline, whereas negative-ratio sensitivity shows that MLP becomes unstable when the sampled candidate distribution shifts and Cross + JK variants provide robust information gain in more uncertain candidate-ranking regimes. Broader comparisons with feature-only, heterogeneous graph, graph-Transformer, and KG-embedding baselines show that feature priors and alternative graph models remain strong in some regimes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results support cross-residual learning as a robust, condition-dependent strategy for integrating multi-source biological evidence in rice gene-trait prioritization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1883598","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T05:31:54Z","timestamp":1786599114000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross-residual knowledge graph learning for robust multi-trait gene\u2013trait prioritization in rice"],"prefix":"10.3389","volume":"17","author":[{"given":"Jingchao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Zhongyuan University of Technology","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science, Zhongyuan University of Technology","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqin","family":"Fu","sequence":"additional","affiliation":[{"name":"Liuzhou Railway Vocational Technical College","place":["Liuzhou, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Mao","sequence":"additional","affiliation":[{"name":"China Construction Urban Science Municipal Survey and Design Co., Ltd.","place":["Lanzhou, Gansu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Liuzhou Railway Vocational Technical College","place":["Liuzhou, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Zhongyuan University of Technology","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenglong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Liuzhou Railway Vocational Technical College","place":["Liuzhou, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirui","family":"Xing","sequence":"additional","affiliation":[{"name":"China Construction Urban Science Municipal Survey and Design Co., Ltd.","place":["Lanzhou, Gansu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuelin","family":"Hu","sequence":"additional","affiliation":[{"name":"Liuzhou Railway Vocational Technical College","place":["Liuzhou, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"B1","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes","year":"2013"},{"key":"B2","article-title":"Relational graph attention networks","author":"Busbridge","year":"2019","journal-title":"arXiv"},{"key":"B3","first-page":"1725","article-title":"Simple and deep graph convolutional networks","author":"Chen","year":"2020"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11573","article-title":"Convolutional 2D knowledge graph embeddings","author":"Dettmers","year":"2018"},{"key":"B5","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi","year":"2023","journal-title":"J. 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