The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An adversarial scheme for integrating multi-modal data on protein function
Open accessPreprint — published as 10.1016/j.cels.2025.101444
Abstract
In order to begin to decipher the structure of the cell, we need to integrate multiple types of data of different scales on subcellular organization. Such integration requires dealing with multiple data modalities and with missing data. To this end, we developed MIRAGE, a multi-modal generative model for integrating protein sequence, protein-protein interaction and protein localization data. Our approach successfully learns a joint embedding space that captures the complex relationships between these diverse modalities. We evaluate our model’s performance against existing methods, obtaining superior performance in several key tasks, including protein function prediction and module detection. MIRAGE source code is available at https://github.com/raminass/MIRAGE .
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 34
- 2022, Flamingo: a visual language model for few-shot learning, Advances in neural information processing systems, 35, 23716
- A. Almahairi , S. Rajeshwar , A. Sordoni , P. Bachman , and A. Courville. Augmented cyclegan: Learning many-to-many mappings from unpaired data. In International conference on machine learning, pages 195–204. PMLR, 2018.
- R. Arandjelovic and A. Zisserman . Look, listen and learn. In Proceedings of the IEEE international conference on computer vision, pages 609–617, 2017. · DOI
- 10.1038/75556 · DOI · Paper page
- 2018, Multimodal machine learning: A survey and taxonomy, IEEE transactions on pattern analysis and machine intelli-gence, 41, 423
- 10.1038/s41587-022-01251-z · DOI
- 10.1109/TPAMI.2013.50 · DOI · Paper page
- 10.1088/1742-5468/2008/10/P10008 · DOI · Paper page
- 10.1016/j.cviu.2018.10.009 · DOI · Paper page
- T. Chen , S. Kornblith , M. Norouzi , and G. Hinton . A simple framework for con-trastive learning of visual representations. In International conference on machine learning, pages 1597–1607. PMLR, 2020.
- D. T. Forster , S. C. Li , Y. Yashiroda , M. Yoshimura , Z. Li , L. A. V. Isuhuaylas , K. Itto-Nakama , D. Yamanaka , Y. Ohya , H. Osada , et al. Bionic: biological network integration using convolutions. Nature Methods, pages 1–12, 2022. · DOI
- R. Girdhar , A. El-Nouby , Z. Liu , M. Singh , K. V. Alwala , A. Joulin , and I. Misra . Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180–15190, 2023. · DOI · Paper page
- 10.1093/nar/gky973 · DOI · Paper page
- X. Glorot and Y. Bengio . Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on arti-ficial intelligence and statistics, pages 249–256. JMLR Workshop and Conference Proceedings, 2010.
- I. Goodfellow , J. Pouget-Abadie , M. Mirza , B. Xu , D. Warde-Farley , S. Ozair , A. Courville , and Y. Bengio . Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
- A. Grover and J. Leskovec . node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 855–864, New York, NY, USA, Aug. 2016. Association for Computing Machinery. · DOI · Paper page
- I. Gulrajani , F. Ahmed , M. Arjovsky , V. Dumoulin , and A. C. Courville . Improved training of wasserstein gans. Advances in neural information processing systems, 30, 2017.
- M. Heusel , H. Ramsauer , T. Unterthiner , B. Nessler , and S. Hochreiter . Gans trained by a two time-scale update rule converge to a local nash equilibrium. Ad-vances in neural information processing systems, 30, 2017.
- 10.1016/j.cell.2021.04.011 · DOI · Paper page
- 10.1093/nar/28.1.27 · DOI · Paper page
- 2014, Adam: A method for stochastic optimization, arXiv preprint
- 10.3390/biology11081208 · DOI
- 10.1109/jproc.2015.2460697 · DOI · Paper page
- 10.1126/science.ade2574 · DOI · Paper page
- X. Mao , Q. Li , H. Xie , R. Y. Lau , Z. Wang , and S. Paul Smolley . Least squares gen-erative adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2794–2802, 2017. · DOI · Paper page
- 2018, Umap: Uniform manifold approximation and projection for dimension reduction, arXiv preprint
- 10.1093/bioinformatics/btae387 · DOI
- J. Ngiam , A. Khosla , M. Kim , J. Nam , H. Lee , and A. Y. Ng . Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 689–696, 2011.
- 10.1038/s41592-019-0658-6 · DOI · Paper page
- 2015, Unsupervised representation learning with deep convolutional gener-ative adversarial networks, arXiv preprint
- 10.1126/science.aal3321 · DOI · Paper page
- T. Wang and P. Isola . Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning, pages 9929–9939. PMLR, 2020.
- 10.1186/s13059-020-02238-2 · DOI
- J.-Y. Zhu , T. Park , P. Isola , and A. A. Efros . Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE interna-tional conference on computer vision, pages 2223–2232, 2017. · DOI · Paper page
Metadata
- Authors
- Nasser, R., Schaffer, L. V., Ideker, T., & Sharan, R.
- Affiliations
- Tel Aviv University (IL)
- University of California San Diego (US)
- Corresponding author
- Sharan, Roded
- Venue
- Cell Systems
- Publisher
- openRxiv
- Published
- 2025-01-20
- Volume
- 16
- Issue
- 11
- Pages
- 101444-101444
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W4416377949
- Cited by
- 2
- References
- 1
- Citations by year
- 2026: 2
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Israel Science Foundation (1692/24)
- Tel Aviv University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- multimodal data integration, protein function prediction, adversarial learning, generative models, protein-protein interaction, protein localization, joint embedding space, protein complex detection, subcellular organization, mirage, HEK293T cells
- MeSH terms
- Algorithms, Humans, Proteins, Computational Biology, HEK293 Cells, Protein Interaction Maps
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
