Daniel Urda

2.4k total citations
48 papers, 1.2k citations indexed

About

Daniel Urda is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Daniel Urda has authored 48 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 11 papers in Computer Vision and Pattern Recognition and 10 papers in Artificial Intelligence. Recurrent topics in Daniel Urda's work include Gene expression and cancer classification (7 papers), Machine Learning in Bioinformatics (5 papers) and Anomaly Detection Techniques and Applications (5 papers). Daniel Urda is often cited by papers focused on Gene expression and cancer classification (7 papers), Machine Learning in Bioinformatics (5 papers) and Anomaly Detection Techniques and Applications (5 papers). Daniel Urda collaborates with scholars based in Spain, United States and United Kingdom. Daniel Urda's co-authors include Martial Hebert, J. Andrew Bagnell, Nicolas Vandapel, José M. Jerez, Leonardo Franco, Ignacio J. Turias, Juan Jesús Ruíz-Aguilar, Xuehan Xiong, Rafael Marcos Luque‐Baena and Bernabè Dorronsoro and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Sensors.

In The Last Decade

Daniel Urda

41 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Urda Spain 17 332 309 285 223 151 48 1.2k
Xudong Sun China 16 105 0.3× 618 2.0× 359 1.3× 96 0.4× 93 0.6× 63 1.7k
Xiaofeng Zhang China 19 162 0.5× 452 1.5× 448 1.6× 35 0.2× 114 0.8× 95 1.6k
Shengwei Tian China 22 122 0.4× 728 2.4× 646 2.3× 58 0.3× 53 0.4× 137 2.0k
Geremy Heitz United States 10 116 0.3× 461 1.5× 1.0k 3.7× 90 0.4× 125 0.8× 12 1.6k
Hong Gu China 19 80 0.2× 169 0.5× 281 1.0× 50 0.2× 69 0.5× 72 1.0k
Feng Wen China 19 89 0.3× 302 1.0× 147 0.5× 91 0.4× 288 1.9× 118 1.2k
Xiao Ling China 20 92 0.3× 379 1.2× 978 3.4× 68 0.3× 92 0.6× 65 1.7k
Giovanni Aloisio Italy 17 92 0.3× 98 0.3× 113 0.4× 32 0.1× 96 0.6× 172 1.2k
Guobao Xiao China 22 62 0.2× 1.1k 3.5× 298 1.0× 67 0.3× 502 3.3× 91 1.8k
Wenhui Li China 18 82 0.2× 616 2.0× 336 1.2× 53 0.2× 141 0.9× 135 1.2k

Countries citing papers authored by Daniel Urda

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Urda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Urda

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Urda. A scholar is included among the top collaborators of Daniel Urda 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 Daniel Urda. Daniel Urda 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.
Sanz, Juan M., et al.. (2025). Dataset for defect detection in textile manufacturing. Data in Brief. 59. 111451–111451.
2.
Urda, Daniel, et al.. (2025). Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review. Frontiers in Artificial Intelligence. 8. 1481338–1481338.
3.
Benavente‐Fernández, Isabel, et al.. (2024). Neurodevelopmental Impairments Prediction in Premature Infants Based on Clinical Data and Machine Learning Techniques. SHILAP Revista de lepidopterología. 7(3). 685–696.
4.
Cambra, Carlos, et al.. (2023). A Clustering Extension of HUEPs for the Analysis of Performance Anomalies in Robots. Cybernetics & Systems. 55(6). 1357–1377.
5.
Urda, Daniel, et al.. (2022). An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation. Sustainability. 14(2). 777–777. 5 indexed citations
6.
Magán‐Carrión, Roberto, et al.. (2021). Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration. arXiv (Cornell University). 8 indexed citations
7.
8.
Ruíz-Aguilar, Juan Jesús, et al.. (2021). A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain). Stochastic Environmental Research and Risk Assessment. 35(10). 1999–2019. 13 indexed citations
9.
Urda, Daniel, et al.. (2021). Deep neural networks architecture driven by problem-specific information. Neural Computing and Applications. 33(15). 9403–9423. 8 indexed citations
10.
Magán‐Carrión, Roberto, et al.. (2020). Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches. Applied Sciences. 10(5). 1775–1775. 75 indexed citations
11.
Ruíz-Aguilar, Juan Jesús, et al.. (2020). A permutation entropy-based EMD–ANN forecasting ensemble approach for wind speed prediction. Neural Computing and Applications. 33(7). 2369–2391. 63 indexed citations
12.
Ruíz-Aguilar, Juan Jesús, et al.. (2020). A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities. Applied Sciences. 10(23). 8326–8326. 6 indexed citations
13.
Ruíz-Aguilar, Juan Jesús, et al.. (2019). A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models. Neurocomputing. 391. 282–291. 9 indexed citations
14.
Urda, Daniel, Nicolas Vandapel, & Martial Hebert. (2018). Directional Associative Markov Network for 3-D Point Cloud Classification. Figshare. 23 indexed citations
15.
Vilaj, Marija, Daniel Urda, Felix Agakov, et al.. (2017). IgG glycan patterns are associated with type 2 diabetes in independent European populations. Biochimica et Biophysica Acta (BBA) - General Subjects. 1861(9). 2240–2249. 93 indexed citations
16.
Ribelles, Nuria, et al.. (2016). Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science. PLoS ONE. 11(8). e0161135–e0161135. 2 indexed citations
17.
Luque‐Baena, Rafael Marcos, et al.. (2013). Analysis of Cancer Microarray Data using Constructive Neural Networks and Genetic Algorithms.. 55–63. 18 indexed citations
18.
Jerez, José M., et al.. (2012). Improving Motivation in Learning Programming Skills for Engineering Students. International journal of engineering education. 28(1). 202–208. 7 indexed citations
19.
Urda, Daniel, et al.. (2012). WIMP: Web server tool for missing data imputation. Computer Methods and Programs in Biomedicine. 108(3). 1247–1254. 4 indexed citations
20.
Urda, Daniel, et al.. (2012). Addressing critical issues in the development of an Oncology Information System. International Journal of Medical Informatics. 82(5). 398–407. 18 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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