Ross D. King

10.5k total citations
153 papers, 5.7k citations indexed

About

Ross D. King is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Ross D. King has authored 153 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 99 papers in Molecular Biology, 54 papers in Computational Theory and Mathematics and 39 papers in Artificial Intelligence. Recurrent topics in Ross D. King's work include Computational Drug Discovery Methods (43 papers), Machine Learning in Bioinformatics (25 papers) and Microbial Metabolic Engineering and Bioproduction (22 papers). Ross D. King is often cited by papers focused on Computational Drug Discovery Methods (43 papers), Machine Learning in Bioinformatics (25 papers) and Microbial Metabolic Engineering and Bioproduction (22 papers). Ross D. King collaborates with scholars based in United Kingdom, Sweden and United States. Ross D. King's co-authors include Michael J.E. Sternberg, Stephen Muggleton, Ashwin Srinivasan, Larisa Soldatova, Amanda Clare, Mohammed Ouali, Douglas B. Kell, Stephen G. Oliver, Kenneth E. Whelan and Luc De Raedt and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Ross D. King

146 papers receiving 5.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ross D. King 2.9k 1.5k 1.2k 590 526 153 5.7k
Jinyan Li 3.5k 1.2× 1.8k 1.2× 1.3k 1.1× 1.3k 2.2× 1.3k 2.6× 488 9.5k
Michael Schroeder 5.2k 1.8× 2.8k 1.9× 2.1k 1.8× 1.9k 3.1× 612 1.2× 224 12.8k
Lukasz Kurgan 8.6k 3.0× 1.9k 1.3× 1.4k 1.2× 568 1.0× 1.9k 3.6× 255 12.2k
Doheon Lee 4.1k 1.4× 873 0.6× 925 0.8× 247 0.4× 165 0.3× 229 6.9k
Egon Willighagen 3.3k 1.1× 699 0.5× 2.2k 1.8× 354 0.6× 682 1.3× 126 6.0k
Marinka Žitnik 2.9k 1.0× 1.8k 1.2× 1.5k 1.3× 265 0.4× 438 0.8× 84 6.1k
Sanghyun Park 2.5k 0.9× 746 0.5× 346 0.3× 250 0.4× 382 0.7× 245 4.9k
Tatsuya Akutsu 7.4k 2.5× 884 0.6× 1.4k 1.2× 139 0.2× 364 0.7× 378 9.2k
Limsoon Wong 4.7k 1.6× 1.7k 1.1× 1.2k 1.1× 749 1.3× 69 0.1× 303 7.8k
Zhu‐Hong You 9.0k 3.1× 1.2k 0.8× 2.5k 2.1× 626 1.1× 406 0.8× 293 12.3k

Countries citing papers authored by Ross D. King

Since Specialization
Citations

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

Fields of papers citing papers by Ross D. King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ross D. King. 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 Ross D. King. The network helps show where Ross D. King may publish in the future.

Co-authorship network of co-authors of Ross D. King

This figure shows the co-authorship network connecting the top 25 collaborators of Ross D. King. A scholar is included among the top collaborators of Ross D. King 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 Ross D. King. Ross D. King 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.
Musslick, Sebastian, Fernand Gobet, Thomas L. Griffiths, et al.. (2025). Automating the practice of science: Opportunities, challenges, and implications. Proceedings of the National Academy of Sciences. 122(5). e2401238121–e2401238121. 7 indexed citations
2.
Mahdizadeh, Sayyed Jalil, Ievgeniia Tiukova, Renan Vinícius de Araújo, et al.. (2024). An experimental target-based platform in yeast for screening Plasmodium vivax deoxyhypusine synthase inhibitors. PLoS neglected tropical diseases. 18(12). e0012690–e0012690. 1 indexed citations
3.
Tiukova, Ievgeniia, et al.. (2024). An automated positive selection screen in yeast provides support for boron-containing compounds as inhibitors of SARS-CoV-2 main protease. Microbiology Spectrum. 12(10). e0124924–e0124924. 1 indexed citations
4.
Wang, Sheldon, et al.. (2023). Dynamics of pump jacks with theories and experiments. 11. 100097–100097. 1 indexed citations
5.
Orhobor, Oghenejokpeme I., et al.. (2023). Protein–ligand binding affinity prediction exploiting sequence constituent homology. Bioinformatics. 39(8). 2 indexed citations
6.
Orhobor, Oghenejokpeme I., Nastasiya F. Grinberg, Larisa Soldatova, & Ross D. King. (2022). Imbalanced regression using regressor-classifier ensembles. Machine Learning. 112(4). 1365–1387. 2 indexed citations
7.
Orhobor, Oghenejokpeme I., et al.. (2022). A simple spatial extension to the extended connectivity interaction features for binding affinity prediction. Royal Society Open Science. 9(5). 211745–211745. 3 indexed citations
8.
Orhobor, Oghenejokpeme I., Nickolai Alexandrov, & Ross D. King. (2020). Predicting rice phenotypes with meta and multi-target learning. Machine Learning. 109(11). 2195–2212. 3 indexed citations
9.
King, Ross D.. (2017). The Adam and Eve Robot Scientists for the Automated Discovery of Scientific Knowledge. Bulletin of the American Physical Society. 2017. 1 indexed citations
10.
Jensen, Elaine, Michael Squance, Astley Hastings, et al.. (2011). Understanding the value of hydrothermal time on flowering in Miscanthus species. Aspects of applied biology. 112(112). 181–189. 6 indexed citations
11.
Srinivasan, Ashwin & Ross D. King. (2008). Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. Journal of Machine Learning Research. 9(48). 1475–1533. 6 indexed citations
12.
Camacho, Rui, Ross D. King, & Ashwin Srinivasan. (2004). Inductive logic programming : 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004 : proceedings. DIAL (Catholic University of Leuven). 1 indexed citations
13.
Coghill, George M., Simon Garrett, & Ross D. King. (2004). Learning qualitative metabolic models. European Conference on Artificial Intelligence. 445–449. 12 indexed citations
14.
Ferré, Sébastien & Ross D. King. (2004). A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics. Fundamenta Informaticae. 66(1). 1–32. 2 indexed citations
15.
Srinivasan, Ashwin, Ross D. King, & Michael Bain. (2003). An empirical study of the use of relevance information in inductive logic programming. Journal of Machine Learning Research. 4. 369–383. 10 indexed citations
16.
Reiser, Philip G. K., Ross D. King, Douglas B. Kell, et al.. (2001). Developing a logical model of yeast metabolism. University of Salford Institutional Repository (University of Salford). 5. 223–244. 14 indexed citations
17.
Kell, Douglas B., Douglas B. Kell, Ross D. King, & Ross D. King. (2000). On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends in biotechnology. 18(3). 93–98. 62 indexed citations
18.
Raedt, Luc De, Hannu Toivonen, & Ross D. King. (1998). Finding frequent substructures in chemical compounds. Lirias (KU Leuven). 30–36. 119 indexed citations
19.
Srinivasan, Ashwin, Ross D. King, Stephen Muggleton, & Michael J.E. Sternberg. (1997). The predictive toxicology evaluation challenge. International Journal of Biological Macromolecules. 50(4). 4–9. 57 indexed citations
20.
King, Ross D.. (1991). PROMIS: experiments in machine learning and protein folding. 291–310. 1 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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