[1]GUO Maozu,ZHOU Aoyu,DUAN Ran.Isoform function prediction based on attention mechanism and multiple instance learning[J].CAAI Transactions on Intelligent Systems,2025,20(6):1508-1519.[doi:10.11992/tis.202410005]
Copy

Isoform function prediction based on attention mechanism and multiple instance learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1508-1519 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2025-11-05
Title:
Isoform function prediction based on attention mechanism and multiple instance learning
Author(s):
GUO Maozu1; 2;  ZHOU Aoyu1; 2;  DUAN Ran1; 2
1. School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
2. Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture, Beijing University of Civil Engineering
Keywords:
gene functions; high-resolution annotation; isoform functions; graph convolutional network; gene ontology embedding; isoform interaction network; fusion network; attention-weighted; loss function
CLC:
TP181
DOI:
10.11992/tis.202410005
Abstract:
High-resolution annotation of gene functions is essential in functional genomics. Multiple isoforms are generated from a single gene via alternative splicing, thereby producing protein variants that contribute to functional diversity. This paper introduces LossIsoFun, a framework for high-resolution isoform function annotation. First, gene ontology (GO) and a graph convolutional network (GCN) are used to preserve hierarchical and semantic structures, producing compressed GO annotations. Then, isoform interaction, coexpression, and sequence similarity networks are integrated to construct an isoform functional network. The isoform sequence data and functional network are fed into a GCN to generate low-dimensional isoform representations. By leveraging gene–isoform relationships, gene function representations are derived. A novel loss function minimizes differences between compressed GO annotations and gene function representations. Finally, isoform functions are annotated by decompressing these representations. Validation on human benchmark datasets demonstrates that LossIsoFun effectively yields isoform function annotation.
References:
[1] PAN Qun, SHAI O, LEE L J, et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing[J]. Nature genetics, 2008, 40(12): 1413-1415.
[2] WANG E T, SANDBERG R, LUO Shujun, et al. Alternative isoform regulation in human tissue transcriptomes[J]. Nature, 2008, 456(7221): 470-476.
[3] CROWL S, COLEMAN M B, CHAPIV A, et al. Systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms using PTM-POSE[EB/OL]. (2024-01-11)[2025-09-15]. https://doi.org/10.1101/2024.01.10.575062.
[4] SMITH L M, KELLEHER N L. Proteoforms as the next proteomics currency[J]. Science, 2018, 359(6380): 1106-1107.
[5] 曾杰. 基于深度多示例学习的可变剪接异构体相互作用预测研究[D]. 重庆: 西南大学, 2021.
ZENG Jie. Study on interaction prediction of alternative splicing isomers based on deep multi-instance learning[D]. Chongqing: Southwest University, 2021.
[6] HOWES A, ROGERSON C, BELYAEV N, et al. The FAM13A long isoform regulates cilia movement and coordination in airway mucociliary transport[J]. American journal of respiratory cell and molecular biology, 2024, 71(3): 282-293.
[7] MITTENDORF K F, DEATHERAGE C L, OHI M D, et al. Tailoring of membrane proteins by alternative splicing of pre-mRNA[J]. Biochemistry, 2012, 51(28): 5541-5556.
[8] GUO Miao, LIU Wei, SERRA S, et al. FGFR2 isoforms support epithelial-stromal interactions in thyroid cancer progression[J]. Cancer research, 2012, 72(8): 2017-2027.
[9] WANG Shiying, SUN Boyun, YUAN Jianye, et al. The different effects of VEGFA121 and VEGFA165 on regulating angiogenesis depend on phosphorylation sites of VEGFR2[J]. Inflammatory bowel diseases, 2017, 23(4): 603-616.
[10] HASSN MESRATI M, SYAFRUDDIN S E, MOHTAR M A, et al. CD44: a multifunctional mediator of cancer progression[J]. Biomolecules, 2021, 11(12): 1850.
[11] REVIL T, TOUTANT J, SHKRETA L, et al. Protein kinase C-dependent control of Bcl-x alternative splicing[J]. Molecular and cellular biology, 2007, 27(24): 8431-8441.
[12] ASHBURNER M, BALL C A, BLAKE J A, et al. Gene ontology: tool for the unification of biology[J]. Nature genetics, 2000, 25(1): 25-29
[13] ZHAO Yingwen, WANG Jun, GUO Maozu, et al. Cross-species protein function prediction with asynchronous-random walk[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2019, 18(4): 1439-1450.
[14] ZHAO Yingwen, FU Guangyuan, WANG Jun, et al. Gene function prediction based on gene ontology hierarchy preserving hashing[J]. Genomics, 2019, 111(3): 334-342.
[15] YU Guoxian, WANG Keyao, FU Guangyuan, et al. NMFGO: gene function prediction via nonnegative matrix factorization with gene ontology[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2020, 17(1): 238-249.
[16] CARBONNEAU M A, CHEPLYGINA V, GRANGER E, et al. Multiple instance learning: a survey of problem characteristics and applications[J]. Pattern recognition, 2018, 77: 329-353.
[17] CHEN Hao, SHAW D, ZENG Jianyang, et al. DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning[J]. Bioinformatics, 2019, 35(14): i284-i294.
[18] LI Wenyuan, KANG Shuli, LIU Chunchi, et al. High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method[J]. Nucleic acids research, 2014, 42(6): e39.
[19] YU Guoxian, WANG Keyao, DOMENICONI C, et al. Isoform function prediction based on bi-random walks on a heterogeneous networkFree[J]. Bioinformatics, 2020, 36(1): 303-310.
[20] SHAW D, CHEN Hao, JIANG Tao. DeepIsoFun: a deep domain adaptation approach to predict isoform functionsFree[J]. Bioinformatics, 2018, 35(15): 2535-2544.
[21] LI Hongdong, YANG Changhuo, ZHANG Zhimin, et al. IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation[J]. Bioinformatics, 2021, 37(4): 522-530.
[22] QIU Sichao, YU Guoxian, LU Xudong, et al. Isoform function prediction by gene ontology embedding[J]. Bioinformatics, 2022, 38(19): 4581-4588.
[23] YU Guoxian, ZHOU Guangjie, ZHANG Xiangliang, et al. DMIL-IsoFun: predicting isoform function using deep multi-instance learning[J]. Bioinformatics, 2021, 37(24): 4818-4825.
[24] 王可尧. 基于RNA-seq数据的可变剪接异构体功能预测方法研究[D]. 重庆: 西南大学, 2019.
WANG Keyao. Study on function prediction method of alternative splicing isomers based on RNA-seq data[D]. Chongqing: Southwest University, 2019.
[25] SU Yaqi, YU Zhejian, JIN Siqian, et al. Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data[J]. Nature communications, 2024, 15(1): 3972.
[26] WANG Keyao, WANG Jun, DOMENICONI C, et al. Differentiating isoform functions with collaborative matrix factorization[J]. Bioinformatics, 2020, 36(6): 1864-1871.
[27] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2016-09-09)[2025-09-15]. https://arxiv.org/abs/1609.02907.
[28] 张硕. 基于图神经网络的剪接异构体功能预测方法研究[D]. 长沙: 中南大学, 2022.
ZHANG Shuo. Study on function prediction method of splicing isomers based on graph neural network[D]. Changsha: Central South University, 2022.
[29] GAO Tianyu, YAO Xingcheng, CHEN Danqi. SimCSE: simple contrastive learning of sentence embeddings[EB/OL]. (2021-04-18)[2025-09-15]. https://arxiv.org/abs/2104.08821.
[30] ZHAO Yingwen, WANG Jun, CHEN Jian, et al. A literature review of gene function prediction by modeling gene ontology[J]. Frontiers in genetics, 2020, 11: 400.
[31] LIN Dekang. An information-theoretic definition of similarity[C]//Proceedings of the Fifteenth International Conference on Machine Learning. Madison: Morgan Kaufmann Publishers Inc. , 1998: 296-304.
[32] LUO Tingjin, ZHANG Weizhong, QIU Shuang, et al. Functional annotation of human protein coding isoforms via non-convex multi-instance learning[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax: ACM, 2017: 345-354.
[33] ALTSCHUL S F, GISH W, MILLER W, et al. Basic local alignment search tool[J]. Journal of molecular biology, 1990, 215(3): 403-410.
[34] RAPPOPORT N, SHAMIR R. NEMO: cancer subtyping by integration of partial multi-omic data[J]. Bioinformatics, 2019, 35(18): 3348-3356.
[35] WANG Bo, MEZLINI A M, DEMIR F, et al. Similarity network fusion for aggregating data types on a genomic scale[J]. Nature methods, 2014, 11(3): 333-337.
[36] 赵璐, 袁立明, 郝琨. 多示例学习算法综述[J]. 计算机科学, 2022, 49(S1): 93-99.
ZHAO Lu, YUAN Liming, HAO Kun. A survey of multi-instance learning algorithms[J]. Computer science, 2022, 49(S1): 93-99.
[37] EKSI R, LI Hongdong, MENON R, et al. Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data[J]. PLoS comput biol, 2013, 9(11): e1003314.
[38] ZHANG Shijia, LIU Huili, YUAN Li, et al. Recognition of CCA1 alternative protein isoforms during temperature acclimation[J]. Plant cell reports, 2021, 40(2): 421-432.
[39] LANGFELDER P, HORVATH S. WGCNA: an R package for weighted correlation network analysis[J]. BMC bioinformatics, 2008, 9: 559.
[40] HUERTA-CEPAS J, SZKLARCZYK D, HELLER D, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses[J]. Nucleic acids research, 2019, 47(D1): D309-D314.
[41] CONSORTIUM U. UniProt: the universal protein knowledgebase in 2021[J]. Nucleic acids research, 2021, 49(D1): D480-D489.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems