The DOI matches its Crossref record; 181 of the 181 references it cites were found in a registry or an index.
Machine learning for perturbational single-cell omics
Open accessAbstract
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Research works printed but not linked: 4
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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,694
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“3).”
Self-citationDissecting cell identity via network inference and in silico gene perturbation
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Impact of the Human Cell Atlas on medicine
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Metadata
- Authors
- Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Theis, Fabian J.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 12
- Issue
- 6
- Pages
- 522-537
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W3170265427
- Cited by
- 137
- References
- 174
- Citations by year
- 2026: 37 · 2025: 29 · 2024: 28 · 2023: 26 · 2022: 14 · 2021: 3
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 380 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- Site cant be reached (submittedVersion, other-oa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- BMBF Berlin
- Helmholtz Association of German Research Centres (ZT-I-007, ZT-I-PF-5-01)
- Silicon Valley Community Foundation
- Bundesministerium für Bildung und Forschung (01IS18036B, 01IS18053A)
- Chan Zuckerberg Initiative (2019- 002438)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell omics, deep learning, cell phenotype, multi-omics integration
- MeSH terms
- Machine Learning, Neural Networks, Computer
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