MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification
Open accessAbstract
High-throughput sequencing methods have brought about a huge change in omics-based biomedical study. Integrating various omics data is possibly useful for identifying some correlations across data modalities, thus improving our understanding of the underlying biological mechanisms and complexity. Nevertheless, most existing graph-based feature extraction methods overlook the complementary information and correlations across modalities. Moreover, these methods tend to treat the features of each omics modality equally, which contradicts current biological principles. To solve these challenges, we introduce a novel approach for integrating multi-omics data termed Multi-Omics hypeRgraph integration nEtwork (MORE). MORE initially constructs a comprehensive hyperedge group by extensively investigating the informative correlations within and across modalities. Subsequently, the multi-omics hypergraph encoding module is employed to learn the enriched omics-specific information. Afterward, the multi-omics self-attention mechanism is then utilized to adaptatively aggregate valuable correlations across modalities for representation learning and making the final prediction. We assess MORE’s performance on datasets characterized by message RNA (mRNA) expression, Deoxyribonucleic Acid (DNA) methylation, and microRNA (miRNA) expression for Alzheimer’s disease, invasive breast carcinoma, and glioblastoma. The results from three classification tasks highlight the competitive advantage of MORE in contrast with current state-of-the-art (SOTA) methods. Moreover, the results also show that MORE has the capability to identify a greater variety of disease-related biomarkers compared to existing methods, highlighting its advantages in biomedical data mining and interpretation. Overall, MORE can be investigated as a valuable tool for facilitating multi-omics analysis and novel biomarker discovery. Our code and data can be publicly accessed at https://github.com/Wangyuhanxx/MORE.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multi-layer network construction and inference often require high-performance infrastructure, restricting participation to well-resourced institutions.
“Multi-layer network construction and inference often require high-performance infrastructure, restricting participation to well-resourced institutions260.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [260]
The Methods sections describe hypergraph construction and model optimization, but do not state that these require high-performance infrastructure. The Conclusion discusses limitations involving other data modalities, not access for less-resourced institutions. Searches for computational resources, hardware, and institutional access found no passage addressing the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,737 · see citing works
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., Sabedot, T. S., Malta, T. M., Pagnotta, S. M., Castiglioni, I., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1507 · Cited by 4,137 · see citing works
5th International Conference on Learning Representations (ICLR 17)
Havrylov, S., & Titov, I. (2017) · Cited by 3,503 · see citing works
ToppGene Suite for gene list enrichment analysis and candidate gene prioritization
Chen, J., Bardes, E. E., Aronow, B. J., & Jegga, A. G. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp427 · Cited by 2,952 · see citing works
Efficient kNN classification with different numbers of nearest neighbors
Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2673241 · Cited by 1,378 · see citing works
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · see citing works
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,079 · see citing works
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., & Huang, K. (2021) · Nature Communications · doi:10.1038/s41467-021-23774-w · Cited by 671 · see citing works
HGNN + : General Hypergraph Neural Networks
Gao, Y., Feng, Y., Ji, S., & Ji, R. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3182052 · Cited by 583 · see citing works
Improved Random Forest for Classification
Paul, A., Mukherjee, D. P., Das, P., Gangopadhyay, A., Chintha, A. R., & Kundu, S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2834830 · Cited by 483 · see citing works
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 474 · see citing works
Neural-network feature selector
Setiono, R., & Liu, H. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572104 · Cited by 381 · see citing works
The landscape of the mesenchymal signature in brain tumours
Behnan, J., Finocchiaro, G., & Hanna, G. (2019) · Brain · doi:10.1093/brain/awz044 · Cited by 346 · see citing works
Ning, L., Zhou, Y., Sun, H. L., Zhang, Y., Shen, C., Wang, Z., Xuan, B., Zhao, Y., Ma, Y., Yan, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42788-0 · Cited by 296 · see citing works
Self-citationDeep learning methods for molecular representation and property prediction
Li, Z., Jiang, M., Wang, S., & Zhang, S. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103373 · Cited by 285 · see citing works
Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer’s disease
Stevenson‐Hoare, J. O., Heslegrave, A., Leonenko, G., Fathalla, D., Bellou, E., Luckcuck, L., Marshall, I. J., Sims, R., Morgan, B. P., Hardy, J., et al. (2022) · Brain · doi:10.1093/brain/awac128 · Cited by 185 · see citing works
Hodes, R. J., & Buckholtz, N. S. (2015) · Expert Opinion on Therapeutic Targets · doi:10.1517/14728222.2016.1135132 · Cited by 177 · see citing works
A benchmark study of deep learning-based multi-omics data fusion methods for cancer
Leng, D., Zheng, L., Wen, Y., Zhang, Y., Wu, L., Wang, J., Wang, M., Zhang, Z., He, S., & Bo, X. (2022) · Genome biology · doi:10.1186/s13059-022-02739-2 · Cited by 163 · see citing works
Multi-omics integration—a comparison of unsupervised clustering methodologies
Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 150 · see citing works
Nagaraj, S., Laskowska-Kaszub, K., Dębski, K. J., Wojsiat, J., Dąbrowski, M., Gabryelewicz, T., Kuźnicki, J., & Wojda, U. (2017) · Oncotarget · doi:10.18632/oncotarget.15109 · Cited by 147 · see citing works
Phosphatidylinositol-3-kinase and AKT1 mutations occur early in breast carcinoma
Dunlap, J. B., Le, C., Shukla, A., Patterson, J., Presnell, A., Heinrich, M. C., Corless, C. L., & Troxell, M. L. (2009) · Breast Cancer Research and Treatment · doi:10.1007/s10549-009-0406-1 · Cited by 121 · see citing works
Better prediction by use of co‐data: adaptive group‐regularized ridge regression
van de Wiel, M. A., Lien, T. G., Verlaat, W., van Wieringen, W. N., & Wilting, S. M. (2015) · Statistics in Medicine · doi:10.1002/sim.6732 · Cited by 113 · see citing works
Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants
Mustaqeem, A., Anwar, S. M., & Majid, M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/7310496 · Cited by 107 · see citing works
A Random Forest classifier-based approach in the detection of abnormalities in the retina
Chowdhury, A. R., Chatterjee, T., & Banerjee, S. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1878-0 · Cited by 105 · see citing works
Multi-omics data integration methods and their applications in psychiatric disorders
Sathyanarayanan, A., Mueller, T. T., Moni, M. A., Schueler, K., Baune, B. T., Lió, P., Mehta, D., Baune, B. T., Dierssen, M., Ebert, B., et al. (2023) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2023.01.001 · Cited by 90 · see citing works
Hamed, A., Griffiths, A. B., Tilby, M. J., Westley, B. R., & May, F. E. B. (2012) · American Journal Of Pathology · doi:10.1016/j.ajpath.2011.11.022 · Cited by 90 · see citing works
MiR-205 Dysregulations in Breast Cancer: The Complexity and Opportunities
Xiao, Y., Humphries, B. A., Yang, C., & Wang, Z. (2019) · Non-Coding RNA · doi:10.3390/ncrna5040053 · Cited by 87 · see citing works
Self-citationNeural network-based approaches for biomedical relation classification: A review
Zhang, Y., Lin, H., Yang, Z., Wang, J., Sun, Y., Xu, B., & Zhao, Z. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103294 · Cited by 85 · see citing works
Robust Distance Measures for k NN Classification of Cancer Data
Ehsani, R., & Drabløs, F. (2020) · Cancer Informatics · doi:10.1177/1176935120965542 · Cited by 82 · see citing works
Dietrich, D., Lesche, R., Tetzner, R., Krispin, M., Dietrich, J., Haedicke, W., Schuster, M., & Kristiansen, G. (2009) · Journal of Histochemistry & Cytochemistry · doi:10.1369/jhc.2009.953026 · Cited by 81 · see citing works
Tumor suppressor role of miR-503
Gupta, G., Chellappan, D. K., Pinto, T. D. J. A., Hansbro, P. M., Bebawy, M., & Dua, K. (2018) · Panminerva Medica · doi:10.23736/s0031-0808.17.03386-9 · Cited by 75 · see citing works
Efficient self-attention mechanism and structural distilling model for Alzheimer’s disease diagnosis
Zhu, J., Tan, Y. H. V., Lin, R., Miao, J., Fan, X., Zhu, Y., Liang, P., Gong, J., & He, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105737 · Cited by 71 · see citing works
Wu, Q., Tian, P., He, D., Jia, Z., He, Y., Luo, W., Lv, X., Wang, Y., Zhang, P., Liang, Y., et al. (2023) · Cell Research · doi:10.1038/s41422-023-00810-6 · Cited by 69 · see citing works
Self-citationGong, P., Cheng, L., Zhang, Z., Meng, A., Li, E., Chen, J., & Zhang, L. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107377 · Cited by 65 · see citing works
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design
Li, X., Liu, X., Lü, L., Hua, X., Chi, Y., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac231 · Cited by 63 · see citing works
Apelin/APJ system: A novel promising target for neurodegenerative diseases
Luo, H., Han, L., & Xu, J. (2019) · Journal of Cellular Physiology · doi:10.1002/jcp.29001 · Cited by 61 · see citing works
Amyloid beta-mediated KIF5A deficiency disrupts anterograde axonal mitochondrial movement
Wang, Q., Tian, J., Chen, H., Du, H., & Guo, L. (2019) · Neurobiology of Disease · doi:10.1016/j.nbd.2019.03.021 · Cited by 56 · see citing works
Ouyang, D., Liang, Y., Li, L., Ai, N., Lu, S., Yu, M., Liu, X., & Xie, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107303 · Cited by 56 · see citing works
Dartier, J., Lemaitre, E., Chourpa, I., Goupille, C., Servais, S., Chevalier, S., Mahéo, K., & Dumas, J. (2017) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2017.02.019 · Cited by 53 · see citing works
Pan, T., Li, C., Bi, Y., Wang, Z., Gasser, R. B., Purcell, A. W., Akutsu, T., Webb, G. I., Imoto, S., & Song, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad094 · Cited by 49 · see citing works
Self-citationPolyamine biomarkers as indicators of human disease
Amin, M., Tang, S., Shalamanova, L., Taylor, R. L., Wylie, S. R., Abdullah, B. M., & Whitehead, K. A. (2021) · Biomarkers · doi:10.1080/1354750x.2021.1875506 · Cited by 48 · see citing works
Multi-omics integration and epilepsy: Towards a better understanding of biological mechanisms
Chong, D., Jones, N. C., Schittenhelm, R. B., Anderson, A., & Casillas-Espinosa, P. M. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102480 · Cited by 45 · see citing works
Oliemuller, E., Newman, R., Tsang, S. M., Foo, S., Muirhead, G., Noor, F., Haider, S. A., Aurrekoetxea-Rodríguez, I., Vivanco, M. D., & Howard, B. A. (2020) · eLife · doi:10.7554/elife.58374 · Cited by 44 · see citing works
T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations
Wang, F., Pena-Pena, K., Qian, W., & Arce, G. R. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3371382 · Cited by 42 · see citing works
SOX11 promotes invasive growth and ductal carcinoma in situ progression
Oliemuller, E., Kogata, N., Bland, P. J., Kriplani, D., Daley, F. M., Haider, S. A., Shah, V., Sawyer, E. J., & Howard, B. A. (2017) · The Journal of Pathology · doi:10.1002/path.4939 · Cited by 41 · see citing works
Sequencing the next generation of glioblastomas
Jovčevska, I. (2018) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2018.1462759 · Cited by 37 · see citing works
A focus on CXCR4 in Alzheimer's disease
Wang, R., & Li, H. (2017) · Brain Circulation · doi:10.4103/bc.bc_13_17 · Cited by 35 · see citing works
Wang, Z., Gao, Q., Yi, X., Zhang, X., Zhang, Y., Zhang, D., Lió, P., Bain, C., Bassed, R., Li, S., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107733 · Cited by 35 · see citing works
Self-citationCentral-Smoothing Hypergraph Neural Networks for Predicting Drug–Drug Interactions
Nguyễn, Đ. A., Nguyen, C. H., & Mamitsuka, H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3261860 · Cited by 33 · see citing works
Genetic resilience to amyloid related cognitive decline
Initiative, A. D. N., Hohman, T. J., Dumitrescu, L. C., Cox, N. J., & Jefferson, A. L. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9615-5 · Cited by 31 · see citing works
Li, J., Shi, Z., Liu, F., Fang, X., Cao, K., Meng, Y., Zhang, H., Yu, J., Feng, X., Li, Q., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.671333 · Cited by 29 · see citing works
Wang, Z., Bi, Y., Pan, T., Wang, X., Bain, C., Bassed, R., Imoto, S., Yao, J., Daly, R. J., & Song, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad114 · Cited by 26 · see citing works
Self-citationGálvez, J. A., Jalali, A., Ahumada, L. M., Allan F. Simpao, M., & Rehman, M. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0787-3 · Cited by 26 · see citing works
Beyer, S. J., Lakshmanan, A., Liu, Y.-Y., Zhang, X., Wapnir, I., Smolenski, A., & Jhiang, S. M. (2011) · Endocrinology · doi:10.1210/en.2010-0782 · Cited by 25 · see citing works
Shao, P. (2021) · Brain Research · doi:10.1016/j.brainres.2021.147511 · Cited by 24 · see citing works
Research progress of the CXCR4 mechanism in Alzheimer's disease
Wang, Q., Fang, C., Huang, X., & Xue, L. (2022) · Ibrain · doi:10.1002/ibra.12026 · Cited by 24 · see citing works
Yu, Q.-S., Feng, W.-Q., Shi, L., Niu, R., & Liu, J. (2022) · Brain Sciences · doi:10.3390/brainsci12081022 · Cited by 23 · see citing works
LDAEXC: LncRNA–Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier
Lu, C., & Xie, M. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00573-z · Cited by 21 · see citing works
Identifying biomarkers for breast cancer by gene regulatory network rewiring
Wang, Y., & Liu, Z. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04225-1 · Cited by 20 · see citing works
Self-citationMulti-omics approaches to study platelet mechanisms
Solari, F. A., Krahn, D., Swieringa, F., Verhelst, S. H. L., Rassaf, T., Tasdogan, A., Zahedi, R. P., Lorenz, K., Renné, T., Heemskerk, J. W. M., et al. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102253 · Cited by 20 · see citing works
MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes
Zhang, Y., & Kiryu, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac372 · Cited by 19 · see citing works
Boosted neural networks scoring functions for accurate ligand docking and ranking
Ashtawy, H. M., & Mahapatra, N. R. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s021972001850004x · Cited by 18 · see citing works
Al‐Salam, S., Sudhadevi, M., Awwad, A. A. A., & Bashir, M. A. (2019) · BMC Cancer · doi:10.1186/s12885-019-5316-y · Cited by 16 · see citing works
Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG
Kaur, A., Verma, K., Bhondekar, A. P., & Shashvat, K. (2019) · Current Pharmaceutical Biotechnology · doi:10.2174/1389201020666190618112715 · Cited by 13 · see citing works
The Gαq/phospholipase Cβ signaling system represses tau aggregation
Garwain, O., Yerramilli, V. S., Romero, K., & Scarlata, S. (2020) · Cellular Signalling · doi:10.1016/j.cellsig.2020.109620 · Cited by 9 · see citing works
Khairallah, M. I., Kassem, L. A., Yassin, N. A. E., el Din, M. A. G., Zekri, M., & Attia, M. (2016) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2016.01.007 · Cited by 8 · see citing works
An efficient approach for identifying important biomarkers for biomedical diagnosis
Huang, J., Chen, Y., Phoa, F. K. H., Lin, Y.-H., & Lin, S. (2024) · Biosystems · doi:10.1016/j.biosystems.2024.105163 · Cited by 4 · see citing works
Deep learning based method for predicting DNA N6-methyladenosine sites
Han, K., Wang, J., Chu, Y., Liao, Q., Ding, Y., Zheng, D., Wan, J., Guo, X., & Zou, Q. (2024) · Methods · doi:10.1016/j.ymeth.2024.07.012 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
AI‐Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review
Singh, R., Orimi, H. E., Pedabaliyarasimhuni, P. K. R., Hoesli, C. A., & Chioua, M. (2025) · Biotechnology Journal · doi:10.1002/biot.70100 · Cited by 22 · see citing works
Aghziel, A., Mahraz, M. A., Tairi, H., & Aherrahrou, N. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf468 · Cited by 15 · see citing works
Chen, W., & Qiu, H. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109024 · Cited by 6 · see citing works
A Map of the Lipid–Metabolite–Protein Network to Aid Multi-Omics Integration
Anyaegbunam, U. A., Vagiona, A.-C., ten Cate, V., Bauer, K., Schmidlin, T., Distler, U., Tenzer, S., Araldi, E., Bîndilă, L., Wild, P. S., et al. (2025) · Biomolecules · doi:10.3390/biom15040484 · Cited by 5 · see citing works
Yan, Z., Ge, F., Zhang, Y., He, Y., Liu, Y., Zhu, Y., Song, J., & Yu, D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00756 · Cited by 5 · see citing works
Self-citationXiang, Y., Li, J., Hu, X.-D., Yang, X., Sun, F., Yang, J., Ge, W., Zhou, T., & Song, E. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1665228 · Cited by 4 · see citing works
Wang, J., Huang, X., Kuang, H., & Cheng, Y. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00789-1 · Cited by 3 · see citing works
Benchmarking computational methods for multi-omics biomarker discovery in cancer
Li, A. Z., Du, Y., Liu, Y., Chen, L., & Liu, R. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag200 · Cited by 3 · see citing works
Johari, T., & Chicco, D. (2026) · Discover Computing · doi:10.1007/s10791-026-09974-2 · Cited by 2 · see citing works
Zhu, Y.-H., Zhu, S., Yu, X., Yan, H., Liu, Y., Xie, X., Yu, D., & Ye, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf420 · Cited by 1 · see citing works
Self-citationLiu, T., Hounye, A. H., Yin, X., Jiang, X., & Wu, J. (2026) · IRBM · doi:10.1016/j.irbm.2026.100944 · Cited by 1 · see citing works
Dual-Branch Contrastive Network with Deep Separable Convolution for Enhanced 6mA Site Identification
Sun, Y., Wang, Z., Zhang, Y., Song, J., & Yu, D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01058
Self-citationBhole, G., Poorvi, H. C., Madhav, J. R., Vinod, P. K., & Bhimalapuram, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18477-1_59
SimNetX: tinkering with patient similarity networks to understand biomedical data
Anlauf, T., Kubikova, K., Ochodková, E., Kriegová, E., & Kudělka, M. (2025) · Applied Network Science · doi:10.1007/s41109-025-00743-6
Govindarajan, S., & Yadalam, P. K. (2025) · Journal of Interdisciplinary Dentistry · doi:10.4103/jid.jid_154_25
AI in multi-omics analysis in stem cell research
Ghosh, B., Das, G., Pal, S., Khatua, S., & Ghosh, Z. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.01.014
MHGCL: Multimodal heterogeneous graph contrastive learning for cancer patient classification
Chen, W., & Qiu, H. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109127
DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion
Dang, Y., Yan, X., Zhou, L., & Li, D. (2026) · Entropy · doi:10.3390/e28060616
Multi-Omics Cancer Subtyping with Robust Correlation, UMAP, and Topological Hypergraph Learning
Mukhdoomi, M. A., & Chachoo, M. A. (2026) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/3415
Advancing multi-omics analysis via dynamic labeling with shared-specific information
Wu, J., Chen, Z., 蒲雅丽, Lin, W., Dai, Y., Liu, G., Wu, W., & Wang, S. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123935
Multiomic resources and MolluscDB database
Li, Y., Wang, J., Lv, J., Liu, F., & Wang, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33606-5.00006-0
Li, J., Zhao, M.-Y., Xiao, Y., Xie, M.-Y., Jiang, L., Chen, S., Tang, J.-J., & Guo, F. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020099
Ali, R. E., El-Khoribi, R. A., Hassanein, E. E., & Mousa, F. A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111290
Guo, X., Sun, Y., Chen, Y., Liu, H., Liu, X., & Wang, X. (2026) · Biosensors · doi:10.3390/bios16090514
Self-citation
- Authors
- Wang, Y., Wang, Z., Yu, X., Wang, X., Song, J., Yu, D.-J., & Ge, F.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Monash University (AU)
- City University of Hong Kong (HK)
- Nanjing University of Posts and Telecommunications (CN)
- Corresponding author
- Song, Jiangning
- Yu, Dong-Jun
- Ge, Fang
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-11-22
- Volume
- 26
- Issue
- 1
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC11653202
- OpenAlex
- W4405563164
- Cited by
- 25
- References
- 71
- Citations by year
- 2026: 13 · 2025: 12
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CityU Scholars (submittedVersion, cc-by)
- Funders
- Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (NY223062)
- Monash University
- Natural Science Foundation of Jiangsu (BK20201304)
- National Natural Science Foundation of China (62072243, 62372234)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Keywords
- multi-omics integration, hypergraph neural networks, biomarker discovery, disease classification, self-attention, mRNA expression, DNA methylation, microRNA expression, Alzheimer's disease, invasive breast cancer, glioblastoma
- MeSH terms
- Multiomics, Algorithms, Alzheimer Disease, Breast Neoplasms, Glioblastoma, Humans, RNA, Messenger, Biomarkers, Tumor, Biomarkers, DNA Methylation, Computational Biology, Genomics, MicroRNAs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
