Peter K. Koo

1.8k total citations
39 papers, 852 citations indexed

About

Peter K. Koo is a scholar working on Molecular Biology, Biophysics and Artificial Intelligence. According to data from OpenAlex, Peter K. Koo has authored 39 papers receiving a total of 852 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 6 papers in Biophysics and 3 papers in Artificial Intelligence. Recurrent topics in Peter K. Koo's work include Genomics and Chromatin Dynamics (13 papers), RNA and protein synthesis mechanisms (11 papers) and RNA Research and Splicing (8 papers). Peter K. Koo is often cited by papers focused on Genomics and Chromatin Dynamics (13 papers), RNA and protein synthesis mechanisms (11 papers) and RNA Research and Splicing (8 papers). Peter K. Koo collaborates with scholars based in United States, Canada and Romania. Peter K. Koo's co-authors include S. G. J. Mochrie, Sarah M. Schreiner, Yao Zhao, Megan C. King, Sean R. Eddy, Sergey Ovchinnikov, Elizabeth Rhoades, William M. Atkins, Marshal Hedin and Shahan Derkarabetian and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Peter K. Koo

37 papers receiving 843 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peter K. Koo United States 17 619 111 102 53 42 39 852
Corella S. Casas-Delucchi Germany 16 938 1.5× 146 1.3× 73 0.7× 100 1.9× 60 1.4× 23 1.1k
Qiong Yang United States 13 804 1.3× 84 0.8× 163 1.6× 59 1.1× 26 0.6× 36 1.1k
Cheryl A. Telmer United States 17 710 1.1× 62 0.6× 70 0.7× 107 2.0× 80 1.9× 36 986
Claire D. McWhite United States 13 449 0.7× 57 0.5× 84 0.8× 16 0.3× 22 0.5× 18 686
Benedikt Rauscher Germany 10 522 0.8× 83 0.7× 50 0.5× 75 1.4× 40 1.0× 16 731
M. Madan Babu United Kingdom 9 1.2k 1.9× 285 2.6× 37 0.4× 22 0.4× 32 0.8× 11 1.3k
Debashis Barik India 16 331 0.5× 52 0.5× 58 0.6× 15 0.3× 66 1.6× 48 649
Jens Kleinjung United Kingdom 19 1.3k 2.2× 173 1.6× 106 1.0× 27 0.5× 71 1.7× 40 1.6k
Olivier Cinquin United States 15 640 1.0× 96 0.9× 76 0.7× 230 4.3× 37 0.9× 27 1.0k
Guilhem Chalancon United Kingdom 8 932 1.5× 86 0.8× 58 0.6× 19 0.4× 42 1.0× 10 1.0k

Countries citing papers authored by Peter K. Koo

Since Specialization
Citations

This map shows the geographic impact of Peter K. Koo'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 Peter K. Koo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter K. Koo more than expected).

Fields of papers citing papers by Peter K. Koo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter K. Koo. 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 Peter K. Koo. The network helps show where Peter K. Koo may publish in the future.

Co-authorship network of co-authors of Peter K. Koo

This figure shows the co-authorship network connecting the top 25 collaborators of Peter K. Koo. A scholar is included among the top collaborators of Peter K. Koo 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 Peter K. Koo. Peter K. Koo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Thompson, Michael, et al.. (2025). Massive experimental quantification allows interpretable deep learning of protein aggregation. Science Advances. 11(18). eadt5111–eadt5111. 4 indexed citations
2.
3.
Koo, Peter K., et al.. (2024). EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. Bioinformatics. 40(3). 1 indexed citations
4.
Seitz, E., David M. McCandlish, Justin B. Kinney, & Peter K. Koo. (2024). Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models. Nature Machine Intelligence. 6(6). 701–713. 11 indexed citations
5.
Koo, Peter K., et al.. (2023). Current approaches to genomic deep learning struggle to fully capture human genetic variation. Nature Genetics. 55(12). 2021–2022. 7 indexed citations
6.
Koo, Peter K., et al.. (2023). EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations. Genome biology. 24(1). 105–105. 13 indexed citations
7.
Gao, Yuan, Xue‐Yan He, Xiaoli Wu, et al.. (2023). ETV6 dependency in Ewing sarcoma by antagonism of EWS-FLI1-mediated enhancer activation. Nature Cell Biology. 25(2). 298–308. 19 indexed citations
8.
Majdandzic, Antonio, et al.. (2023). Correcting gradient-based interpretations of deep neural networks for genomics. Genome biology. 24(1). 109–109. 17 indexed citations
9.
Kawaguchi, Risa Karakida, et al.. (2022). Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. Briefings in Bioinformatics. 24(1). 2 indexed citations
10.
Bhattacharya, Nicholas, Roshan Rao, Justas Dauparas, et al.. (2022). End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman. Bioinformatics. 39(1). 21 indexed citations
11.
Koo, Peter K., et al.. (2022). Evaluating deep learning for predicting epigenomic profiles. Nature Machine Intelligence. 4(12). 1088–1100. 36 indexed citations
12.
Bhattacharya, Nicholas, Neil Thomas, Roshan Rao, et al.. (2021). Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention. PubMed. 27. 34–45. 15 indexed citations
13.
Koo, Peter K., et al.. (2021). Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks. PLoS Computational Biology. 17(5). e1008925–e1008925. 43 indexed citations
14.
Somerville, Tim D.D., Yali Xu, Xiaoli Wu, et al.. (2020). ZBED2 is an antagonist of interferon regulatory factor 1 and modifies cell identity in pancreatic cancer. Proceedings of the National Academy of Sciences. 117(21). 11471–11482. 38 indexed citations
15.
Koo, Peter K. & Sean R. Eddy. (2019). Representation learning of genomic sequence motifs with convolutional neural networks. PLoS Computational Biology. 15(12). e1007560–e1007560. 53 indexed citations
16.
Koo, Peter K. & S. G. J. Mochrie. (2018). Applying Perturbation Expectation-Maximization to Protein Trajectories of Rho GTPases. Methods in molecular biology. 1821. 57–70. 1 indexed citations
17.
Koo, Peter K. & S. G. J. Mochrie. (2016). Systems-level approach to uncovering diffusive states and their transitions from single-particle trajectories. Physical review. E. 94(5). 52412–52412. 31 indexed citations
18.
Koo, Peter K., et al.. (2015). Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry. PLoS Computational Biology. 11(10). e1004297–e1004297. 22 indexed citations
19.
Schreiner, Sarah M., Peter K. Koo, Yao Zhao, S. G. J. Mochrie, & Megan C. King. (2015). The tethering of chromatin to the nuclear envelope supports nuclear mechanics. Nature Communications. 6(1). 7159–7159. 176 indexed citations
20.
Nath, Abhinav, Adam Trexler, Peter K. Koo, et al.. (2010). Single-Molecule Fluorescence Spectroscopy Using Phospholipid Bilayer Nanodiscs. Methods in enzymology on CD-ROM/Methods in enzymology. 472. 89–117. 49 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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