Jamie Overbeek

2.6k total citations
7 papers, 122 citations indexed

About

Jamie Overbeek is a scholar working on Molecular Biology, Astronomy and Astrophysics and Computational Theory and Mathematics. According to data from OpenAlex, Jamie Overbeek has authored 7 papers receiving a total of 122 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 2 papers in Astronomy and Astrophysics and 2 papers in Computational Theory and Mathematics. Recurrent topics in Jamie Overbeek's work include Bioinformatics and Genomic Networks (2 papers), Astro and Planetary Science (2 papers) and Computational Drug Discovery Methods (2 papers). Jamie Overbeek is often cited by papers focused on Bioinformatics and Genomic Networks (2 papers), Astro and Planetary Science (2 papers) and Computational Drug Discovery Methods (2 papers). Jamie Overbeek collaborates with scholars based in United States, Belgium and China. Jamie Overbeek's co-authors include Alexander Partin, Austin Clyde, Yitan Zhu, Oleksandr Narykov, Rick Stevens, Thomas Brettin, Heather R. Jacobson, Eileen D. Friel, Rory Butler and Ross Overbeek and has published in prestigious journals such as PLoS ONE, The Astrophysical Journal and Cancer Research.

In The Last Decade

Jamie Overbeek

6 papers receiving 118 citations

Peers

Jamie Overbeek

MB

AA

CTM

MC

ECOLO

Yannick Mahlich United States

Steffen Schaper Germany

Dimos Goundaroulis Switzerland

M. Ughetto Sweden

Rao Fu China

В. А. Крылов Russia

Enrique Audain United Kingdom

Taushif Khan United States

Sebastien Baur France

Yannick Mahlich United States

Jamie Overbeek

152 ×2.4

MB

7 ×0.3

AA

15 ×0.6

CTM

10 ×0.8

MC

16 ×1.3

ECOLO

Citations per year, relative to Jamie Overbeek Jamie Overbeek (= 1×) peers Yannick Mahlich

Countries citing papers authored by Jamie Overbeek

Since Specialization

Citations

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

Fields of papers citing papers by Jamie Overbeek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jamie Overbeek. 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 Jamie Overbeek. The network helps show where Jamie Overbeek may publish in the future.

Co-authorship network of co-authors of Jamie Overbeek

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

All Works

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7 of 7 papers shown

1.

Partin, Alexander, Steve Jones, Jamie Overbeek, et al.. (2026). Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis. Briefings in Bioinformatics. 27(1).

2.

Partin, Alexander, Thomas Brettin, Yitan Zhu, et al.. (2023). Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Frontiers in Medicine. 10. 1086097–1086097. 59 indexed citations

3.

Partin, Alexander, Thomas Brettin, Yitan Zhu, et al.. (2023). Abstract 5380: Systematic evaluation and comparison of drug response prediction models: a case study of prediction generalization across cell lines datasets. Cancer Research. 83(7_Supplement). 5380–5380. 1 indexed citations

4.

Zou, Xiaohui, et al.. (2022). Classification of bacterial plasmid and chromosome derived sequences using machine learning. PLoS ONE. 17(12). e0279280–e0279280. 5 indexed citations

5.

Parrello, Bruce, Rory Butler, Philippe Chlenski, et al.. (2019). A machine learning-based service for estimating quality of genomes using PATRIC. BMC Bioinformatics. 20(1). 486–486. 29 indexed citations

6.

Overbeek, Jamie, Eileen D. Friel, & Heather R. Jacobson. (2016). NEW NEUTRON-CAPTURE MEASUREMENTS IN 23 OPEN CLUSTERS. I. THE r-PROCESS. The Astrophysical Journal. 824(2). 75–75. 13 indexed citations

7.

Overbeek, Jamie, Eileen D. Friel, Heather R. Jacobson, et al.. (2014). NGC 7789: AN OPEN CLUSTER CASE STUDY. The Astronomical Journal. 149(1). 15–15. 15 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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