Caroline Uhler

4.1k total citations
63 papers, 1.6k citations indexed

About

Caroline Uhler is a scholar working on Molecular Biology, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Caroline Uhler has authored 63 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 25 papers in Artificial Intelligence and 10 papers in Statistics and Probability. Recurrent topics in Caroline Uhler's work include Bayesian Modeling and Causal Inference (15 papers), Single-cell and spatial transcriptomics (9 papers) and Genomics and Chromatin Dynamics (9 papers). Caroline Uhler is often cited by papers focused on Bayesian Modeling and Causal Inference (15 papers), Single-cell and spatial transcriptomics (9 papers) and Genomics and Chromatin Dynamics (9 papers). Caroline Uhler collaborates with scholars based in United States, Austria and Switzerland. Caroline Uhler's co-authors include G. V. Shivashankar, Stephen E. Fienberg, Aleksandra Slavković, Adityanarayanan Radhakrishnan, Anastasiya Belyaeva, Garvesh Raskutti, Karren Yang, Saradha Venkatachalapathy, Karthik Damodaran and Pantelis R. Vlachas and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Caroline Uhler

59 papers receiving 1.5k citations

Peers

Caroline Uhler

BIOPH

Jan Hasenauer Germany

Tianhai Tian Australia

Adriano Barra Italy

Paul Kirk United Kingdom

Andreas Raue Germany

Guang Yao China

Marcel Schilling Germany

Jonathan Cooper United Kingdom

James Lu United States

Theodore J. Perkins Canada

Jan Hasenauer Germany

Caroline Uhler

1.7k ×2.6

188 ×0.4

126 ×0.4

193 ×1.3

BIOPH

123 ×1.0

Citations per year, relative to Caroline Uhler Caroline Uhler (= 1×) peers Jan Hasenauer

Countries citing papers authored by Caroline Uhler

Since Specialization

Citations

This map shows the geographic impact of Caroline Uhler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Caroline Uhler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caroline Uhler more than expected).

Fields of papers citing papers by Caroline Uhler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caroline Uhler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Caroline Uhler. The network helps show where Caroline Uhler may publish in the future.

Co-authorship network of co-authors of Caroline Uhler

This figure shows the co-authorship network connecting the top 25 collaborators of Caroline Uhler. A scholar is included among the top collaborators of Caroline Uhler based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Caroline Uhler. Caroline Uhler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Carlson, Rebecca J., J. J. Patten, Brian Y. Soong, et al.. (2025). Single-cell image-based screens identify host regulators of Ebola virus infection dynamics. Nature Microbiology. 10(8). 1989–2002. 3 indexed citations

Radhakrishnan, Adityanarayanan, Sam Friedman, Shaan Khurshid, et al.. (2023). Cross-modal autoencoder framework learns holistic representations of cardiovascular state. Nature Communications. 14(1). 2436–2436. 30 indexed citations

Uhler, Caroline, et al.. (2023). The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables. Journal of the Royal Statistical Society Series B (Statistical Methodology). 85(5). 1639–1658. 4 indexed citations

Radhakrishnan, Adityanarayanan, et al.. (2023). Transfer Learning with Kernel Methods. Nature Communications. 14(1). 5570–5570. 19 indexed citations

Braunger, Jana M., et al.. (2023). Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging. Aging Cell. 23(3). e14056–e14056. 3 indexed citations

Belyaeva, Anastasiya, et al.. (2021). DCI: learning causal differences between gene regulatory networks. Bioinformatics. 37(18). 3067–3069. 13 indexed citations

Yang, Karren, Anastasiya Belyaeva, Saradha Venkatachalapathy, et al.. (2021). Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nature Communications. 12(1). 31–31. 92 indexed citations

Belyaeva, Anastasiya, et al.. (2021). Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nature Communications. 12(1). 1024–1024. 37 indexed citations

Uhler, Caroline & G. V. Shivashankar. (2020). Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity. Current Opinion in Solid State and Materials Science. 25(1). 100874–100874. 3 indexed citations

10.

Uhler, Caroline, et al.. (2020). Ordering-Based Causal Structure Learning in the Presence of Latent Variables.. DSpace@MIT (Massachusetts Institute of Technology). 4098–4108. 3 indexed citations

11.

Wang, Yuhao, et al.. (2018). Direct Estimation of Differences in Causal Graphs. DSpace@MIT (Massachusetts Institute of Technology). 31. 3770–3781. 3 indexed citations

12.

Yang, Karren, et al.. (2018). Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions. DSpace@MIT (Massachusetts Institute of Technology). 5541–5550. 3 indexed citations

13.

Radhakrishnan, Adityanarayanan, et al.. (2017). Counting Markov Equivalence Classes by Number of Immoralities.. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations

14.

Yang, Karren, et al.. (2017). Permutation-based Causal Inference Algorithms with Interventions. DSpace@MIT (Massachusetts Institute of Technology). 30. 5822–5831. 6 indexed citations

15.

Radhakrishnan, Adityanarayanan, et al.. (2017). Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis. Scientific Reports. 7(1). 17946–17946. 36 indexed citations

16.

Wang, Yejun, et al.. (2017). Orientation and repositioning of chromosomes correlate with cell geometry–dependent gene expression. Molecular Biology of the Cell. 28(14). 1997–2009. 84 indexed citations

17.

Uhler, Caroline & G. V. Shivashankar. (2017). Regulation of genome organization and gene expression by nuclear mechanotransduction. Nature Reviews Molecular Cell Biology. 18(12). 717–727. 304 indexed citations

18.

Uhler, Caroline, et al.. (2016). EXACT GOODNESS-OF-FIT TESTING FOR THE ISING MODEL. DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations

19.

Uhler, Caroline, et al.. (2016). Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and its Applications. 509. 247–275.

20.

Uhler, Caroline & G. V. Shivashankar. (2016). Geometric control and modeling of genome reprogramming. PubMed. 6(4). 76–84. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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