A configuration optimization approach for reconfigurable manufacturing system based on column-generation combined with graph neural network.

Authors: Cui, Feng¹ (AUTHOR), Jiang, Zhibin^1,2,3 (AUTHOR), Zhou, Xin¹ (AUTHOR), Zheng, Junli^2,3 (AUTHOR), Geng, Na^2,3 (AUTHOR) gengna@sjtu.edu.cn

Source: International Journal of Production Research. Feb2025, Vol. 63 Issue 3, p970-991. 22p.

Subject Terms: *Column generation (Algorithms), *Inventory costs, Graph neural networks, Structural optimization, Stochastic programming

Industrial internet platform-driven decentralised multi-level synchronised reconfiguration of assembly supply chains.

Authors: Huang, Hai-nan¹ (AUTHOR), Qu, Ting¹ (AUTHOR) quting@jnu.edu.cn, Qiu, Xiao-hui¹ (AUTHOR), Ma, Lin^1,2 (AUTHOR), Nie, Duxian³ (AUTHOR), Li, Congdong¹ (AUTHOR), Huang, George Q.^1,2,4 (AUTHOR)

Source: International Journal of Production Research. Jan2025, p1-23. 23p. 10 Illustrations.

Subject Terms: *Technological innovations, *Supply chains, *Information storage & retrieval systems, *Information architecture, Graph neural networks

Modeling and Interpreting the Propagation Influence of Neighbor Information in Time-Variant Networks with Exemplification by Financial Risk Prediction.

Authors: Wang, Jianfei¹ (AUTHOR) wangjianfei9@126.com, Zhou, Lina² (AUTHOR) lzhou8@charlotte.edu, Jiang, Cuiqing^1,3 (AUTHOR) jiangcuiq2017@163.com, Wang, Zhao^1,4 (AUTHOR) xcwangzhao@163.com

Source: Journal of Management Information Systems. 2025, Vol. 42 Issue 1, p105-142. 38p.

Subject Terms: *Decision support systems, *Credit risk, *Artificial intelligence, *Recommender systems, Graph neural networks

Using graph neural network to conduct supplier recommendation based on large-scale supply chain.

Authors: Tu, Yuchun¹ (AUTHOR), Li, Wenxin¹ (AUTHOR), Song, Xiao¹ (AUTHOR), Gong, Kaiqi¹ (AUTHOR) ZB2039102@buaa.edu.cn, Liu, Lu² (AUTHOR), Qin, Yunhao¹ (AUTHOR), Liu, Songsong¹ (AUTHOR), Liu, Ming¹ (AUTHOR)

Source: International Journal of Production Research. Dec2024, Vol. 62 Issue 24, p8595-8608. 14p.

Subject Terms: *Supply chain disruptions, *Recommender systems, *Division of labor, Graph neural networks, Knowledge graphs

Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0.

Authors: Zhang, Chao¹ (AUTHOR), Jing, Yanzhen¹ (AUTHOR), Zhou, Guanghui¹ (AUTHOR) ghzhou@mail.xjtu.edu.cn, Yan, Hairui¹ (AUTHOR), Chang, Fengtian² (AUTHOR)

Source: International Journal of Production Research. Dec2024, p1-17. 17p. 11 Illustrations.

Subject Terms: *Systems engineering, Graph neural networks, Deep learning, Knowledge graphs, Detection algorithms

Towards knowledge graph reasoning for supply chain risk management using graph neural networks.

Authors: Kosasih, Edward Elson¹ (AUTHOR) eek31@cam.ac.uk, Margaroli, Fabrizio² (AUTHOR), Gelli, Simone² (AUTHOR), Aziz, Ajmal¹ (AUTHOR), Wildgoose, Nick³ (AUTHOR), Brintrup, Alexandra¹ (AUTHOR)

Source: International Journal of Production Research. Aug2024, Vol. 62 Issue 15, p5596-5612. 17p.

Subject Terms: *Supply chain management, *Supply chain disruptions, Graph neural networks, Knowledge graphs, Machine learning

Deep learning and sequence mining for manufacturing process and sequence selection.

Authors: Zhao, Changxuan¹ (AUTHOR) czhao33@gatech.edu, Dinar, Mahmoud² (AUTHOR), Melkote, Shreyes N.¹ (AUTHOR)

Source: International Journal of Production Research. Jul2024, Vol. 62 Issue 14, p5293-5314. 22p.

Subject Terms: *Manufacturing processes, *Artificial neural networks, Convolutional neural networks, Graph neural networks, Deep learning

CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associations.

Authors: Pan, Yiru¹ (AUTHOR), Ji, Xingyu¹ (AUTHOR), You, Jiaqi¹ (AUTHOR), Li, Lu¹ (AUTHOR), Liu, Zhenping¹ (AUTHOR), Zhang, Xianlong¹ (AUTHOR), Zhang, Zeyu¹ (AUTHOR), Wang, Maojun¹ (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-16. 16p.

Subject Terms: Graph neural networks, Emmer wheat, Bipartite graphs, Genetic transcription regulation, Rapeseed

BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction.

Authors: Yang, Yang^1,2,3 (AUTHOR), Zheng, Zixuan³ (AUTHOR), Xu, Yuyang³ (AUTHOR), Wei, Huifang⁴ (AUTHOR), Yan, Wenying^2,4,5 (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-10. 10p.

Subject Terms: *Artificial neural networks, *Artificial intelligence, Graph neural networks, Language models, Knowledge graphs

Predicting transcriptional changes induced by molecules with MiTCP.

Authors: Yang, Kaiyuan¹ (AUTHOR), Cheng, Jiabei¹ (AUTHOR), Cao, Shenghao¹ (AUTHOR), Pan, Xiaoyong¹ (AUTHOR), Shen, Hong-Bin¹ (AUTHOR), Yuan, Ye^1,2 (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-9. 9p.

Subject Terms: Graph neural networks, Molecular structure, Drug discovery, Small molecules, Gene expression

Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Authors: Cai, Lingsheng¹ (AUTHOR), Ma, Xiuli¹ (AUTHOR), Ma, Jianzhu^2,3 (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-12. 12p.

Subject Terms: *Databases, Graph neural networks, Gene expression, Deep learning, Multiomics

Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF.

Authors: Li, Juntao¹ (AUTHOR), Xiang, Shan¹ (AUTHOR), Wei, Dongqing² (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-13. 13p.

Subject Terms: Graph neural networks, Matrix decomposition, Nonnegative matrices, Cell communication, Breast cancer

Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.

Authors: Li, Xingyi^1,2,3 (AUTHOR), Xu, Jialuo¹ (AUTHOR), Li, Junming^2,4 (AUTHOR), Gu, Jia⁴ (AUTHOR), Shang, Xuequn¹ (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-11. 11p.

Subject Terms: Graph neural networks, Convolutional neural networks, Cancer genes, Gene regulatory networks, Biological networks, Deep learning

R3Design: deep tertiary structure-based RNA sequence design and beyond.

Authors: Tan, Cheng^1,2 (AUTHOR), Zhang, Yijie^3,4 (AUTHOR), Gao, Zhangyang² (AUTHOR), Cao, Hanqun⁵ (AUTHOR), Li, Siyuan² (AUTHOR), Ma, Siqi² (AUTHOR), Blanchette, Mathieu^3,4 (AUTHOR), Li, Stan Z² (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-12. 12p.

Subject Terms: *Artificial intelligence, Graph neural networks, Nucleotide sequence, RNA, Tertiary structure, Synthetic biology

GADIFF: a transferable graph attention diffusion model for generating molecular conformations.

Authors: Wang, Donghan¹ (AUTHOR), Dong, Xu¹ (AUTHOR), Zhang, Xueyou¹ (AUTHOR), Hu, LiHong¹ (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-12. 12p.

Subject Terms: Graph neural networks, Molecular conformation, Molecular weights, Dihedral angles, Bond angles

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Authors: Yuan, Lin^1,2,3 (AUTHOR), Sun, Shengguo^1,2,3 (AUTHOR), Jiang, Yufeng^1,2,3 (AUTHOR), Zhang, Qinhu⁴ (AUTHOR), Ye, Lan⁵ (AUTHOR), Zheng, Chun-Hou⁶ (AUTHOR), Huang, De-Shuang^4,7 (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-14. 14p.

Subject Terms: Graph neural networks, Machine learning, Deep learning, RNA sequencing, Gene expression

ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.

Authors: Cai, Lijun¹ (AUTHOR), Yue, Guanyu¹ (AUTHOR), Chen, Yifan¹ (AUTHOR), Wang, Li² (AUTHOR), Yao, Xiaojun³ (AUTHOR), Zou, Quan⁴ (AUTHOR), Fu, Xiangzheng^1,5 (AUTHOR), Cao, Dongsheng⁶ (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-10. 10p.

Subject Terms: Graph neural networks, Protein structure, Deep learning, Amino acid sequence, Drug development

The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.

Authors: Wei, Yuxiao¹ (AUTHOR), Zhang, Qi² (AUTHOR), Liu, Liwei² (AUTHOR)

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-11. 11p.

Subject Terms: Graph neural networks, De Bruijn graph, LincRNA, Non-coding RNA, Graph algorithms

Forecasting cryptocurrency volatility: a novel framework based on the evolving multiscale graph neural network.

Authors: Zhou, Yang^1,2 (AUTHOR), Xie, Chi^1,2 (AUTHOR) xiechi@hnu.edu.cn, Wang, Gang-Jin^1,2 (AUTHOR), Gong, Jue^1,2 (AUTHOR), Zhu, You^1,2 (AUTHOR)

Source: Financial Innovation. 2/24/2025, Vol. 11 Issue 1, p1-52. 52p.

Subject Terms: *Financial markets, *Marketing management, *Econometrics, Graph neural networks, Deep learning

Accurate POI recommendation for random groups with improved graph neural networks and a multi-negotiation model.

Authors: Song, Xiaoyu¹ (AUTHOR), Liu, Zhizhong¹ (AUTHOR) zhizhongliu@ytu.edu.cn, Meng, Lingqiang¹ (AUTHOR), Chu, Dianhui² (AUTHOR), Yu, Jian³ (AUTHOR), Sheng, Quan Z.⁴ (AUTHOR)

Source: Scientific Reports. 3/4/2025, Vol. 15 Issue 1, p1-22. 22p.

Subject Terms: *GRAPH neural networks, *ARTIFICIAL intelligence, *BIPARTITE graphs, *MULTIAGENT systems, *INFORMATION networks