Goran Nenadić

5.5k total citations · 1 hit paper
170 papers, 3.5k citations indexed

About

Goran Nenadić is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Goran Nenadić has authored 170 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Artificial Intelligence, 100 papers in Molecular Biology and 14 papers in Information Systems. Recurrent topics in Goran Nenadić's work include Biomedical Text Mining and Ontologies (94 papers), Topic Modeling (58 papers) and Natural Language Processing Techniques (46 papers). Goran Nenadić is often cited by papers focused on Biomedical Text Mining and Ontologies (94 papers), Topic Modeling (58 papers) and Natural Language Processing Techniques (46 papers). Goran Nenadić collaborates with scholars based in United Kingdom, Australia and United States. Goran Nenadić's co-authors include ‪Irena Spasić, John Keane, Martin Gerner, Casey Bergman, Michael Krauthammer, Sophia Ananiadou, Adrian Stetco, Fateme Dinmohammadi, David Flynn and Xingyu Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Goran Nenadić

157 papers receiving 3.3k citations

Hit Papers

Machine learning methods for wind turbine condition monit... 2018 2026 2020 2023 2018 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine learning methods for wind turbine condition monitoring: A review
    2018 588 citations Adrian Stetco, Fateme Dinmohammadi et al. Renewable Energy profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Goran Nenadić United Kingdom 29 1.6k 1.5k 385 222 199 170 3.5k
Ioannis Tsamardinos Greece 30 2.8k 1.8× 1.7k 1.2× 186 0.5× 414 1.9× 169 0.8× 135 5.6k
Marko Robnik‐Šikonja Slovenia 17 1.9k 1.2× 586 0.4× 208 0.5× 455 2.0× 111 0.6× 86 4.2k
Paulo Lisböa United Kingdom 31 1.1k 0.7× 396 0.3× 110 0.3× 220 1.0× 205 1.0× 181 3.6k
Alexander Statnikov United States 30 1.6k 1.0× 1.8k 1.2× 96 0.2× 262 1.2× 88 0.4× 83 4.6k
Randall Wald United States 20 1.5k 0.9× 368 0.3× 147 0.4× 609 2.7× 187 0.9× 70 3.3k
Raheel Nawaz United Kingdom 29 1.1k 0.7× 238 0.2× 203 0.5× 453 2.0× 95 0.5× 161 2.9k
Ilias Maglogiannis Greece 32 1.2k 0.7× 286 0.2× 109 0.3× 490 2.2× 291 1.5× 263 4.3k
Shaker El–Sappagh Egypt 39 2.3k 1.5× 434 0.3× 117 0.3× 577 2.6× 1.0k 5.2× 146 5.3k
Mihail Popescu United States 30 968 0.6× 288 0.2× 344 0.9× 121 0.5× 190 1.0× 237 3.9k
José Neves Portugal 23 1.8k 1.1× 131 0.1× 433 1.1× 200 0.9× 192 1.0× 255 3.5k

Countries citing papers authored by Goran Nenadić

Since Specialization
Citations

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

Fields of papers citing papers by Goran Nenadić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Goran Nenadić

This figure shows the co-authorship network connecting the top 25 collaborators of Goran Nenadić. A scholar is included among the top collaborators of Goran Nenadić 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 Goran Nenadić. Goran Nenadić 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.
Nenadić, Goran, et al.. (2025). LT3: Generating Medication Prescriptions with Conditional Transformer. Research Explorer (The University of Manchester). 205–218.
2.
Nenadić, Goran, et al.. (2025). Beyond Reconstruction: Generating Privacy-Preserving Clinical Letters. 60–74. 1 indexed citations
3.
4.
Romero, Pablo, et al.. (2025). Medication Extraction and Entity Linking using Stacked and Voted Ensembles on LLMs. Leiden Repository (Leiden University). 303–315.
5.
6.
Han, Soyeon Caren, et al.. (2024). MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media. Robotics. 13(3). 53–53. 2 indexed citations
7.
Jani, Meghna, Maksim Belousov, Yuanyuan Zhang, et al.. (2024). Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients. Annals of the Rheumatic Diseases. 83(8). 1082–1091. 3 indexed citations
8.
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Nenadić, Goran, et al.. (2023). Student’s t-Distribution: On Measuring the Inter-Rater Reliability When the Observations are Scarce. Research Explorer (The University of Manchester). 419–428. 1 indexed citations
10.
11.
Karystianis, George, Armita Adily, Peter R. Schofield, et al.. (2022). Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study. JMIR Formative Research. 6(10). e39373–e39373.
12.
Hassan, Lamiece, et al.. (2022). Text mining tweets on e-cigarette risks and benefits using machine learning following a vaping related lung injury outbreak in the USA. SHILAP Revista de lepidopterología. 2. 100066–100066. 4 indexed citations
13.
Hassan, Lamiece, Goran Nenadić, & Mary P. Tully. (2020). A Social Media Campaign (#datasaveslives) to Promote the Benefits of Using Health Data for Research Purposes: Mixed Methods Analysis. Journal of Medical Internet Research. 23(2). e16348–e16348. 5 indexed citations
14.
Karystianis, George, Armita Adily, Peter W. Schofield, et al.. (2019). Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study. Journal of Medical Internet Research. 21(3). e13067–e13067. 28 indexed citations
15.
Stetco, Adrian, Fateme Dinmohammadi, Xingyu Zhao, et al.. (2018). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy. 133. 620–635. 588 indexed citations breakdown →
16.
Karystianis, George, et al.. (2017). Automatic mining of symptom severity from psychiatric evaluation notes. International Journal of Methods in Psychiatric Research. 27(1). 24 indexed citations
17.
Belousov, Maksim, et al.. (2017). Frequent discussion of insomnia and weight gain with glucocorticoid therapy: an analysis of Twitter posts. npj Digital Medicine. 1(1). 31 indexed citations
18.
Flórez-Vargas, Oscar, Andy Brass, George Karystianis, et al.. (2016). Bias in the reporting of sex and age in biomedical research on mouse models. eLife. 5. 78 indexed citations
19.
Kovačević, Aleksandar, et al.. (2015). Combining knowledge- and data-driven methods for de-identification of clinical narratives. Journal of Biomedical Informatics. 58. S53–S59. 42 indexed citations
20.
Karystianis, George, et al.. (2015). Using local lexicalized rules to identify heart disease risk factors in clinical notes. Journal of Biomedical Informatics. 58. S183–S188. 22 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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