Ana Cernea

636 total citations
25 papers, 273 citations indexed

About

Ana Cernea is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Geophysics. According to data from OpenAlex, Ana Cernea has authored 25 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Geophysics. Recurrent topics in Ana Cernea's work include Bioinformatics and Genomic Networks (4 papers), Gene expression and cancer classification (4 papers) and COVID-19 epidemiological studies (3 papers). Ana Cernea is often cited by papers focused on Bioinformatics and Genomic Networks (4 papers), Gene expression and cancer classification (4 papers) and COVID-19 epidemiological studies (3 papers). Ana Cernea collaborates with scholars based in Spain, United States and France. Ana Cernea's co-authors include Juan Luis Fernández‐Martínez, Enrique J. deAndrés‐Galiana, Óscar Álvarez-Machancoses, Andrzej Kloczkowski, Anam Naz, Faryal Mehwish Awan, Mubashir Hassan, Zulima Fernández‐Muñiz, Lourdes Villalustre Martínez and Stephen T. Sonis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Biophysical Journal and International Journal of Molecular Sciences.

In The Last Decade

Ana Cernea

25 papers receiving 258 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ana Cernea Spain 8 82 41 38 36 27 25 273
Safiye Çelik United States 8 146 1.8× 47 1.1× 25 0.7× 53 1.5× 41 1.5× 11 316
Óscar Álvarez-Machancoses Spain 8 93 1.1× 21 0.5× 15 0.4× 37 1.0× 41 1.5× 13 276
Lorenz Adlung Germany 8 243 3.0× 23 0.6× 55 1.4× 58 1.6× 22 0.8× 15 455
Giovanna Nicora Italy 9 167 2.0× 57 1.4× 22 0.6× 79 2.2× 41 1.5× 35 434
Smarti Reel United Kingdom 4 299 3.6× 71 1.7× 33 0.9× 40 1.1× 16 0.6× 5 537
Kruthi Suvarna Japan 7 88 1.1× 32 0.8× 28 0.7× 54 1.5× 40 1.5× 11 237
Fleur Jeanquartier Austria 11 94 1.1× 33 0.8× 32 0.8× 67 1.9× 39 1.4× 17 302
Margaretha Stenmarker Sweden 4 58 0.7× 16 0.4× 22 0.6× 28 0.8× 50 1.9× 7 309
Xiaowei Xu China 10 38 0.5× 18 0.4× 12 0.3× 61 1.7× 33 1.2× 34 435
Shang Xue China 5 48 0.6× 31 0.8× 16 0.4× 53 1.5× 58 2.1× 9 238

Countries citing papers authored by Ana Cernea

Since Specialization
Citations

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

Fields of papers citing papers by Ana Cernea

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ana Cernea

This figure shows the co-authorship network connecting the top 25 collaborators of Ana Cernea. A scholar is included among the top collaborators of Ana Cernea 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 Ana Cernea. Ana Cernea 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.
Fernández‐Muñiz, Zulima, et al.. (2024). Measuring Dental Chamber Volume with DICOM Images from Cone-Beam Computed Tomography Can Be Improved with a Simple Algorithm. Applied Sciences. 14(13). 5365–5365. 1 indexed citations
2.
Cernea, Ana, et al.. (2023). Comparison of three mathematical models for COVID-19 prediction. Biophysical Journal. 122(3). 284a–284a. 3 indexed citations
3.
Fernández‐Muñiz, Zulima, et al.. (2023). Three Mathematical Models for COVID-19 Prediction. Mathematics. 11(3). 506–506. 5 indexed citations
4.
Hassan, Mubashir, Faryal Mehwish Awan, Anam Naz, et al.. (2022). Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. International Journal of Molecular Sciences. 23(9). 4645–4645. 111 indexed citations
5.
Fernández‐Martínez, Juan Luis, Zulima Fernández‐Muñiz, Ana Cernea, & Andrzej Kloczkowski. (2021). Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic. Computational and Mathematical Methods in Medicine. 2021. 1–14. 5 indexed citations
6.
Álvarez-Machancoses, Óscar, et al.. (2020). On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine. SHILAP Revista de lepidopterología. 7 indexed citations
7.
Cernea, Ana, Juan Luis Fernández‐Martínez, Enrique J. deAndrés‐Galiana, et al.. (2020). Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer. BMC Bioinformatics. 21(S2). 89–89. 7 indexed citations
8.
Álvarez-Machancoses, Óscar, et al.. (2020). <p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>. Pharmacogenomics and Personalized Medicine. Volume 13. 105–119. 20 indexed citations
9.
Fernández‐Martínez, Juan Luis, et al.. (2019). Robust Sampling of Defective Pathways in Multiple Myeloma. International Journal of Molecular Sciences. 20(19). 4681–4681. 4 indexed citations
10.
Cernea, Ana, Juan Luis Fernández‐Martínez, Enrique J. deAndrés‐Galiana, et al.. (2019). Prognostic networks for unraveling the biological mechanisms of Sarcopenia. Mechanisms of Ageing and Development. 182. 111129–111129. 10 indexed citations
11.
Fernández‐Martínez, Juan Luis, Zulima Fernández‐Muñiz, Shan Xu, et al.. (2019). Efficient uncertainty analysis of the 3D electrical tomography inverse problem. Geophysics. 84(3). E209–E223. 1 indexed citations
12.
Eiró, Noemí, Sandra Cid, María Fraile, et al.. (2019). MMP11 expression in intratumoral inflammatory cells in breast cancer. Histopathology. 75(6). 916–930. 27 indexed citations
13.
Pallero, J.L.G., Zulima Fernández‐Muñiz, Ana Cernea, et al.. (2018). Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems. Entropy. 20(2). 96–96. 16 indexed citations
14.
15.
Álvarez-Machancoses, Óscar, et al.. (2018). Principal component analysis in protein tertiary structure prediction. Journal of Bioinformatics and Computational Biology. 16(2). 1850005–1850005. 3 indexed citations
16.
Fernández‐Martínez, Juan Luis, et al.. (2017). The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia. Journal of Molecular and Genetic Medicine. 11(4). 2 indexed citations
17.
Reinbolt, Raquel E., Stephen T. Sonis, Cynthia Timmers, et al.. (2017). Genomic risk prediction of aromatase inhibitor‐related arthralgia in patients with breast cancer using a novel machine‐learning algorithm. Cancer Medicine. 7(1). 240–253. 23 indexed citations
18.
Fernández‐Martínez, Juan Luis & Ana Cernea. (2015). Exploring the Uncertainty Space of Ensemble Classifiers in Face Recognition. International Journal of Pattern Recognition and Artificial Intelligence. 29(3). 1556002–1556002. 4 indexed citations
19.
García–Gonzalo, Esperanza, Juan Luis Fernández‐Martínez, & Ana Cernea. (2014). Four-Points Particle Swarm Optimization Algorithms. 22. 239–266. 3 indexed citations
20.
Cernea, Ana, et al.. (2013). Connectivist Learning Objects and Learning Styles. Interdisciplinary Journal of e-Skills and Lifelong Learning. 9. 105–124. 8 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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