Paloma Maı́n

685 total citations
24 papers, 454 citations indexed

About

Paloma Maı́n is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Paloma Maı́n has authored 24 papers receiving a total of 454 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Statistics and Probability, 9 papers in Artificial Intelligence and 4 papers in Molecular Biology. Recurrent topics in Paloma Maı́n's work include Bayesian Modeling and Causal Inference (8 papers), Advanced Statistical Methods and Models (8 papers) and Statistical Distribution Estimation and Applications (5 papers). Paloma Maı́n is often cited by papers focused on Bayesian Modeling and Causal Inference (8 papers), Advanced Statistical Methods and Models (8 papers) and Statistical Distribution Estimation and Applications (5 papers). Paloma Maı́n collaborates with scholars based in Spain, Switzerland and United Kingdom. Paloma Maı́n's co-authors include Kevin Cowtan, Miguel A. Gómez–Villegas, Hilario Navarro, Morven Leese, Angelo Gámez‐Pozo, Enrique Espinosa, Juan Ángel Fresno Vara, Jorge M. Arevalillo, Mariana Díaz‐Almirón and Rocío López‐Vacas and has published in prestigious journals such as PLoS ONE, Cancer Research and Scientific Reports.

In The Last Decade

Paloma Maı́n

24 papers receiving 436 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paloma Maı́n Spain 11 192 80 67 59 58 24 454
Jyothi Subramanian India 9 299 1.6× 40 0.5× 77 1.1× 58 1.0× 93 1.6× 9 602
Quan Du United States 5 418 2.2× 64 0.8× 22 0.3× 53 0.9× 60 1.0× 6 521
Kaitlyn Gayvert United States 8 377 2.0× 90 1.1× 15 0.2× 44 0.7× 35 0.6× 18 607
Sonia Cerquozzi United States 9 515 2.7× 33 0.4× 28 0.4× 57 1.0× 28 0.5× 21 944
Samson Fong United States 7 510 2.7× 67 0.8× 9 0.1× 49 0.8× 100 1.7× 11 790
Zhaleh Safikhani Canada 11 543 2.8× 38 0.5× 25 0.4× 139 2.4× 211 3.6× 20 861
Petr Smirnov Canada 14 636 3.3× 61 0.8× 25 0.4× 124 2.1× 232 4.0× 25 969
Raziur Rahman United States 9 191 1.0× 51 0.6× 14 0.2× 10 0.2× 26 0.4× 19 340
Jamie Munro United Kingdom 3 160 0.8× 36 0.5× 20 0.3× 59 1.0× 24 0.4× 4 411
In Sock Jang United States 13 522 2.7× 38 0.5× 9 0.1× 150 2.5× 144 2.5× 20 654

Countries citing papers authored by Paloma Maı́n

Since Specialization
Citations

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

Fields of papers citing papers by Paloma Maı́n

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paloma Maı́n

This figure shows the co-authorship network connecting the top 25 collaborators of Paloma Maı́n. A scholar is included among the top collaborators of Paloma Maı́n 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 Paloma Maı́n. Paloma Maı́n 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.
Trilla‐Fuertes, Lucía, Angelo Gámez‐Pozo, Elena López‐Camacho, et al.. (2020). Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer. BMC Cancer. 20(1). 307–307. 14 indexed citations
2.
Trilla‐Fuertes, Lucía, Angelo Gámez‐Pozo, Jorge M. Arevalillo, et al.. (2020). Bayesian networks established functional differences between breast cancer subtypes. PLoS ONE. 15(6). e0234752–e0234752. 5 indexed citations
3.
Gámez‐Pozo, Angelo, Lucía Trilla‐Fuertes, Jorge M. Arevalillo, et al.. (2019). A novel approach to triple-negative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses. Scientific Reports. 9(1). 1538–1538. 50 indexed citations
4.
Trilla‐Fuertes, Lucía, Angelo Gámez‐Pozo, Mariana Díaz‐Almirón, et al.. (2019). Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities. BMC Cancer. 19(1). 636–636. 13 indexed citations
5.
Gámez‐Pozo, Angelo, Lucía Trilla‐Fuertes, Jorge M. Arevalillo, et al.. (2018). Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer. Oncotarget. 9(45). 27586–27594. 10 indexed citations
6.
Gámez‐Pozo, Angelo, Jorge M. Arevalillo, Paolo Nanni, et al.. (2015). Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications. Cancer Research. 75(11). 2243–2253. 39 indexed citations
7.
Maı́n, Paloma, Jorge M. Arevalillo, & Hilario Navarro. (2015). Local effect of asymmetry deviations from Gaussianity using information-based measures. B002–B002. 2 indexed citations
8.
Gómez–Villegas, Miguel A., Paloma Maı́n, & Paola Viviani. (2014). Sensitivity to evidence in Gaussian Bayesian networks using mutual information. Information Sciences. 275. 115–126. 14 indexed citations
9.
Gómez–Villegas, Miguel A., et al.. (2013). Sensitivity to hyperprior parameters in Gaussian Bayesian networks. Journal of Multivariate Analysis. 124. 214–225. 2 indexed citations
10.
Gómez–Villegas, Miguel A., et al.. (2013). Assessing the effect of kurtosis deviations from Gaussianity on conditional distributions. Applied Mathematics and Computation. 219(21). 10499–10505. 6 indexed citations
11.
Gómez–Villegas, Miguel A., et al.. (2012). The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness. Information Sciences. 222. 439–458. 11 indexed citations
12.
Gómez–Villegas, Miguel A., et al.. (2009). A Bayesian analysis for the multivariate point null testing problem. Statistics. 43(4). 379–391. 10 indexed citations
13.
Gómez–Villegas, Miguel A., et al.. (2008). Extreme inaccuracies in Gaussian Bayesian networks. Journal of Multivariate Analysis. 99(9). 1929–1940. 5 indexed citations
14.
Maı́n, Paloma & Hilario Navarro. (2008). Analyzing the effect of introducing a kurtosis parameter in Gaussian Bayesian networks. Reliability Engineering & System Safety. 94(5). 922–926. 7 indexed citations
15.
Gómez–Villegas, Miguel A., et al.. (2007). Sensitivity Analysis in Gaussian Bayesian Networks Using a Divergence Measure. Communication in Statistics- Theory and Methods. 36(3). 523–539. 15 indexed citations
16.
Gómez–Villegas, Miguel A., et al.. (2003). Asymptotic relationships between posterior probabilities and p-values using the hazard rate. Statistics & Probability Letters. 66(1). 59–66. 4 indexed citations
17.
Gómez–Villegas, Miguel A., et al.. (2002). A SUITABLE BAYESIAN APPROACH IN TESTING POINT NULL HYPOTHESIS: SOME EXAMPLES REVISITED. Communication in Statistics- Theory and Methods. 31(2). 201–217. 15 indexed citations
18.
Leese, Morven & Paloma Maı́n. (1994). THE EFFICIENT COMPUTATION OF UNBIASED MAHALANOBIS DISTANCES AND THEIR INTERPRETATION IN ARCHAEOMETRY*. Archaeometry. 36(2). 307–316. 29 indexed citations
19.
Cowtan, Kevin & Paloma Maı́n. (1993). Improvement of macromolecular electron-density maps by the simultaneous application of real and reciprocal space constraints. Acta Crystallographica Section D Biological Crystallography. 49(1). 148–157. 180 indexed citations
20.
Maı́n, Paloma. (1987). Distribuciones neutras, propensas y resistentes a datos atipicos. Hispana. 2(2). 91–101. 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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