Damian Roqueiro

1.4k total citations
34 papers, 945 citations indexed

About

Damian Roqueiro is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Damian Roqueiro has authored 34 papers receiving a total of 945 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 10 papers in Genetics and 8 papers in Artificial Intelligence. Recurrent topics in Damian Roqueiro's work include Genetic Associations and Epidemiology (6 papers), Endometriosis Research and Treatment (5 papers) and Genetic and phenotypic traits in livestock (4 papers). Damian Roqueiro is often cited by papers focused on Genetic Associations and Epidemiology (6 papers), Endometriosis Research and Treatment (5 papers) and Genetic and phenotypic traits in livestock (4 papers). Damian Roqueiro collaborates with scholars based in Switzerland, United States and Germany. Damian Roqueiro's co-authors include Dominik G. Grimm, Detlef Weigel, Chang Liu, Congmao Wang, Claude Becker, Rebecca Schwab, Christa Lanz, Yang Dai, Diana Monsivais and Serdar E. Bulun and has published in prestigious journals such as Bioinformatics, PLoS ONE and The Plant Cell.

In The Last Decade

Damian Roqueiro

34 papers receiving 933 citations

Peers

Damian Roqueiro

IMMUN

Kaname Kojima Japan

Dominique Mercier France

Tetsuo Otani Japan

Shan Yang China

Yongseok Park United States

Xiaoying Zheng China

Xiguo Yuan China

Joseph Huang United States

Junbai Wang Norway

Tao Meng China

Kaname Kojima Japan

Damian Roqueiro

453 ×1.2

109 ×0.4

IMMUN

66 ×0.2

18 ×0.1

21 ×0.1

Citations per year, relative to Damian Roqueiro Damian Roqueiro (= 1×) peers Kaname Kojima

Countries citing papers authored by Damian Roqueiro

Since Specialization

Citations

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

Fields of papers citing papers by Damian Roqueiro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Damian Roqueiro

This figure shows the co-authorship network connecting the top 25 collaborators of Damian Roqueiro. A scholar is included among the top collaborators of Damian Roqueiro 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 Damian Roqueiro. Damian Roqueiro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Tozzi, Francesca, Yanping Zhang, Ramanathan Narayanan, Damian Roqueiro, & Eoin C. O’Connor. (2025). Forestwalk: A Machine Learning Workflow Brings New Insights Into Posture and Balance in Rodent Beam Walking. European Journal of Neuroscience. 61(5). e70033–e70033. 2 indexed citations

Bartram, Julian, et al.. (2024). Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains. PLoS Computational Biology. 20(4). e1011964–e1011964. 4 indexed citations

Ronchi, Silvia, Michele Fiscella, Karsten Borgwardt, et al.. (2024). DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks. Stem Cell Reports. 19(2). 285–298. 6 indexed citations

Mitamura, Yasutaka, Matthias Reiger, Damir Zhakparov, et al.. (2023). Spatial and single-cell transcriptomics provide insights into the complex inflammatory cell network in atopic dermatitis. Journal of Allergy and Clinical Immunology. 151(2). AB338–AB338. 2 indexed citations

Mitamura, Yasutaka, Matthias Reiger, Yi Xiao, et al.. (2023). Spatial transcriptomics combined with single‐cell RNA‐sequencing unravels the complex inflammatory cell network in atopic dermatitis. Allergy. 78(8). 2215–2231. 54 indexed citations

Chen, Dexiong, Julian Bartram, Silvia Ronchi, et al.. (2023). Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks. Frontiers in Neuroinformatics. 16. 1032538–1032538. 3 indexed citations

Zhakparov, Damir, Damian Roqueiro, Zuzanna Łukasik, et al.. (2022). Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study. Infectious Diseases and Therapy. 12(1). 111–129. 5 indexed citations

Moor, Michael, Max Horn, Bastian Rieck, Damian Roqueiro, & Karsten Borgwardt. (2019). Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis.. arXiv (Cornell University). 13 indexed citations

Roqueiro, Damian, et al.. (2018). Methods and Tools in Genome-wide Association Studies. Methods in molecular biology. 1819. 93–136. 11 indexed citations

10.

Montefiori, Lindsey E., Robert Wuerffel, Damian Roqueiro, et al.. (2016). Extremely Long-Range Chromatin Loops Link Topological Domains to Facilitate a Diverse Antibody Repertoire. Cell Reports. 14(4). 896–906. 48 indexed citations

11.

Wang, Congmao, Chang Liu, Damian Roqueiro, et al.. (2014). Genome-wide analysis of local chromatin packing in Arabidopsis thaliana. Genome Research. 25(2). 246–256. 209 indexed citations

12.

Dyson, Matthew T., Damian Roqueiro, Diana Monsivais, et al.. (2014). Genome-Wide DNA Methylation Analysis Predicts an Epigenetic Switch for GATA Factor Expression in Endometriosis. PLoS Genetics. 10(3). e1004158–e1004158. 157 indexed citations

13.

Mu, Wenbo, Damian Roqueiro, & Yang Dai. (2013). A Local Genetic Algorithm for the Identification of Condition‐Specific MicroRNA‐Gene Modules. The Scientific World JOURNAL. 2013(1). 197406–197406. 1 indexed citations

14.

Afshar, Yalda, Julie M. Hastings, Damian Roqueiro, et al.. (2013). Changes in Eutopic Endometrial Gene Expression During the Progression of Experimental Endometriosis in the Baboon, Papio Anubis1. Biology of Reproduction. 88(2). 44–44. 55 indexed citations

15.

Roqueiro, Damian, Lei Huang, & Yang Dai. (2012). Identifying transcription factors and microRNAs as key regulators of pathways using Bayesian inference on known pathway structures. Proteome Science. 10(S1). S15–S15. 5 indexed citations

16.

Yin, Ping, Damian Roqueiro, Lei Huang, et al.. (2012). Genome-Wide Progesterone Receptor Binding: Cell Type-Specific and Shared Mechanisms in T47D Breast Cancer Cells and Primary Leiomyoma Cells. PLoS ONE. 7(1). e29021–e29021. 68 indexed citations

17.

Afshar, Yalda, Jae‐Wook Jeong, Damian Roqueiro, et al.. (2011). Notch1 mediates uterine stromal differentiation and is critical for complete decidualization in the mouse. The FASEB Journal. 26(1). 282–294. 86 indexed citations

18.

Roqueiro, Damian, Jonna Frasor, & Yang Dai. (2010). BindSDb: A binding-information spatial database. 409. 573–578. 3 indexed citations

19.

Roqueiro, Damian & Valery A. Petrushin. (2007). Counting people using video cameras. International Journal of Parallel Emergent and Distributed Systems. 22(3). 193–209. 27 indexed citations

20.

Allen, Bradley P., Valery A. Petrushin, Damian Roqueiro, & Gang Wei. (2006). Semantic Web Techniques for Searching and Navigating Video Shots in BBC Rushes.. TRECVID. 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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