Alireza Tamaddoni‐Nezhad

1.6k total citations
24 papers, 491 citations indexed

About

Alireza Tamaddoni‐Nezhad is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Alireza Tamaddoni‐Nezhad has authored 24 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 4 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Alireza Tamaddoni‐Nezhad's work include Logic, Reasoning, and Knowledge (4 papers), Machine Learning and Algorithms (3 papers) and Bioinformatics and Genomic Networks (3 papers). Alireza Tamaddoni‐Nezhad is often cited by papers focused on Logic, Reasoning, and Knowledge (4 papers), Machine Learning and Algorithms (3 papers) and Bioinformatics and Genomic Networks (3 papers). Alireza Tamaddoni‐Nezhad collaborates with scholars based in United Kingdom, Switzerland and Italy. Alireza Tamaddoni‐Nezhad's co-authors include Stephen Muggleton, Dianhuan Lin, David A. Bohan, Alan Raybould, Guy Woodward, Corinne Vacher, Alex J. Dumbrell, Antonis Kakas, Raphaël A. G. Chaleil and Ute Schmid and has published in prestigious journals such as PLoS ONE, Journal of Molecular Biology and Trends in Ecology & Evolution.

In The Last Decade

Alireza Tamaddoni‐Nezhad

22 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alireza Tamaddoni‐Nezhad United Kingdom 11 245 119 94 55 50 24 491
Mengmeng Lu United States 14 77 0.3× 54 0.5× 88 0.9× 44 0.8× 73 1.5× 46 527
Christian Colombo Malta 11 120 0.5× 86 0.7× 8 0.1× 83 1.5× 67 1.3× 47 413
Chayant Tantipathananandh United States 7 102 0.4× 44 0.4× 52 0.6× 14 0.3× 55 1.1× 8 516
Gregory T. Sullivan Türkiye 12 112 0.5× 39 0.3× 25 0.3× 11 0.2× 57 1.1× 33 316
Filip Blagojević United States 16 65 0.3× 26 0.2× 89 0.9× 27 0.5× 213 4.3× 24 760
Mikel Egaña Aranguren Spain 10 250 1.0× 76 0.6× 332 3.5× 18 0.3× 83 1.7× 29 482
Daniel S. Myers United States 10 105 0.4× 51 0.4× 192 2.0× 12 0.2× 193 3.9× 15 796
Christopher Town United Kingdom 12 86 0.4× 90 0.8× 54 0.6× 5 0.1× 14 0.3× 25 512
Vladimir Ulyantsev Russia 11 93 0.4× 58 0.5× 127 1.4× 74 1.3× 13 0.3× 40 381
Johann van der Merwe Australia 7 99 0.4× 39 0.3× 32 0.3× 7 0.1× 31 0.6× 17 337

Countries citing papers authored by Alireza Tamaddoni‐Nezhad

Since Specialization
Citations

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

Fields of papers citing papers by Alireza Tamaddoni‐Nezhad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alireza Tamaddoni‐Nezhad. 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 Alireza Tamaddoni‐Nezhad. The network helps show where Alireza Tamaddoni‐Nezhad may publish in the future.

Co-authorship network of co-authors of Alireza Tamaddoni‐Nezhad

This figure shows the co-authorship network connecting the top 25 collaborators of Alireza Tamaddoni‐Nezhad. A scholar is included among the top collaborators of Alireza Tamaddoni‐Nezhad 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 Alireza Tamaddoni‐Nezhad. Alireza Tamaddoni‐Nezhad 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.
2.
Bauer, Roman, et al.. (2025). Numerical-Symbolic Learning from Biomedical Data. 1338–1343. 1 indexed citations
3.
4.
Fallah, Saber, et al.. (2023). Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach. Electronic Proceedings in Theoretical Computer Science. 385. 201–212. 2 indexed citations
5.
Yıldırım, Mustafa, et al.. (2022). Prediction Based Decision Making for Autonomous Highway Driving. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 138–145. 4 indexed citations
6.
O’Connell, John F., et al.. (2022). A data-driven approach for characterising revenues of South-Asian long-haul low-cost carriers per equivalent flight capacity per block hour. Journal of Air Transport Management. 103. 102242–102242. 2 indexed citations
7.
Tamaddoni‐Nezhad, Alireza, et al.. (2020). One-Shot Rule Learning for Challenging Character Recognition. 10–27. 1 indexed citations
8.
Ma, Athen, Clare Gray, Alan Raybould, et al.. (2018). Ecological networks reveal resilience of agro-ecosystems to changes in farming management. Nature Ecology & Evolution. 3(2). 260–264. 21 indexed citations
9.
Bohan, David A., Corinne Vacher, Alireza Tamaddoni‐Nezhad, et al.. (2017). Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks. Trends in Ecology & Evolution. 32(7). 477–487. 117 indexed citations
10.
Muggleton, Stephen, et al.. (2013). Meta-interpretive learning: application to grammatical inference. Machine Learning. 94(1). 25–49. 55 indexed citations
11.
Muggleton, Stephen, Alireza Tamaddoni‐Nezhad, & Francesca A. Lisi. (2012). Inductive logic programming : 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31-August 3, 2011 : revised selected papers. DIAL (Catholic University of Leuven). 2 indexed citations
12.
Sternberg, Michael J.E., Alireza Tamaddoni‐Nezhad, Emily J. Kay, et al.. (2012). Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach. Journal of Molecular Biology. 425(1). 186–197. 16 indexed citations
13.
Muggleton, Stephen, Alireza Tamaddoni‐Nezhad, & Francesca A. Lisi. (2011). Proceedings of the 21st international conference on Inductive Logic Programming. 2 indexed citations
14.
Bohan, David A., et al.. (2011). Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning. PLoS ONE. 6(12). e29028–e29028. 35 indexed citations
15.
Kay, Emily J., Alireza Tamaddoni‐Nezhad, Paul G. Hitchen, et al.. (2010). Systems analysis of bacterial glycomes. Biochemical Society Transactions. 38(5). 1290–1293. 9 indexed citations
16.
Tamaddoni‐Nezhad, Alireza, Raphaël A. G. Chaleil, Antonis Kakas, et al.. (2007). Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology Magazine. 26(2). 37–46. 8 indexed citations
17.
Muggleton, Stephen & Alireza Tamaddoni‐Nezhad. (2007). QG/GA: a stochastic search for Progol. Machine Learning. 70(2-3). 121–133. 12 indexed citations
18.
Tamaddoni‐Nezhad, Alireza, Raphaël A. G. Chaleil, Antonis Kakas, & Stephen Muggleton. (2006). Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning. 64(1-3). 209–230. 42 indexed citations
19.
Tamaddoni‐Nezhad, Alireza, Raphaël A. G. Chaleil, Antonis Kakas, & Stephen Muggleton. (2006). Abduction and Induction for Learning Models of Inhibition in Metabolic Networks. 233–239. 3 indexed citations
20.
Tamaddoni‐Nezhad, Alireza & Stephen Muggleton. (2001). Using Genetic Algorithms for learning clauses in first-order logic. Genetic and Evolutionary Computation Conference. 639–646. 2 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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