Matthew Aguirre

3.0k total citations · 1 hit paper
18 papers, 1.0k citations indexed

About

Matthew Aguirre is a scholar working on Genetics, Molecular Biology and Epidemiology. According to data from OpenAlex, Matthew Aguirre has authored 18 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Genetics, 10 papers in Molecular Biology and 3 papers in Epidemiology. Recurrent topics in Matthew Aguirre's work include Genetic Associations and Epidemiology (8 papers), Bioinformatics and Genomic Networks (6 papers) and Genomics and Rare Diseases (4 papers). Matthew Aguirre is often cited by papers focused on Genetic Associations and Epidemiology (8 papers), Bioinformatics and Genomic Networks (6 papers) and Genomics and Rare Diseases (4 papers). Matthew Aguirre collaborates with scholars based in United States, United Kingdom and Australia. Matthew Aguirre's co-authors include Manuel A. Rivas, Yosuke Tanigawa, Konrad J. Karczewski, François Aguet, Angela Yen, Laura D. Gauthier, Tuuli Lappalainen, Alexandra–Chloé Villani, Andrew Kirby and Manuel A. Rivas and has published in prestigious journals such as Nature, Nature Communications and Bioinformatics.

In The Last Decade

Matthew Aguirre

18 papers receiving 1.0k citations

Hit Papers

Landscape of X chromosome inactivation across human tissues 2017 2026 2020 2023 2017 200 400 600
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. Landscape of X chromosome inactivation across human tissues
    2017 665 citations Taru Tukiainen, Alexandra–Chloé Villani et al. Nature profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Aguirre United States 9 488 381 112 104 88 18 1.0k
Laura D. Gauthier United States 11 418 0.9× 611 1.6× 110 1.0× 99 1.0× 84 1.0× 17 1.1k
Moinak Banerjee India 23 263 0.5× 415 1.1× 120 1.1× 109 1.0× 42 0.5× 85 1.3k
Andrea Byrnes United States 5 390 0.8× 318 0.8× 103 0.9× 98 0.9× 80 0.9× 8 786
Mark Fleharty United States 5 460 0.9× 512 1.3× 102 0.9× 102 1.0× 82 0.9× 5 968
Fabiana B. Kohlrausch Brazil 18 266 0.5× 211 0.6× 78 0.7× 63 0.6× 98 1.1× 37 1.0k
Thomas M. Morgan United States 19 716 1.5× 662 1.7× 117 1.0× 37 0.4× 122 1.4× 42 1.7k
Delilah Zabaneh United Kingdom 14 729 1.5× 282 0.7× 100 0.9× 23 0.2× 87 1.0× 20 1.4k
Juan Castillo‐Fernandez United Kingdom 18 151 0.3× 438 1.1× 56 0.5× 37 0.4× 172 2.0× 30 798
Ewelina Pośpiech Poland 27 650 1.3× 1.2k 3.1× 47 0.4× 109 1.0× 49 0.6× 58 1.9k
Amanda L. Taylor United States 13 111 0.2× 446 1.2× 124 1.1× 64 0.6× 69 0.8× 17 1.2k

Countries citing papers authored by Matthew Aguirre

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Aguirre

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Aguirre

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

All Works

18 of 18 papers shown
1.
Aguirre, Matthew, Jeffrey P. Spence, Guy Sella, & Jonathan K. Pritchard. (2025). Gene regulatory network structure informs the distribution of perturbation effects. PLoS Computational Biology. 21(9). e1013387–e1013387. 2 indexed citations
2.
Kumbier, Karl, Aldo Córdova‐Palomera, Matthew Aguirre, et al.. (2024). Learning epistatic polygenic phenotypes with Boolean interactions. PLoS ONE. 19(4). e0298906–e0298906. 2 indexed citations
3.
Yu, Mengyao, Andrew R. Harper, Matthew Aguirre, et al.. (2023). Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy. Circulation Genomic and Precision Medicine. 16(3). 207–215. 3 indexed citations
4.
Smail, Craig, Nicole M. Ferraro, Qin Hui, et al.. (2022). Integration of rare expression outlier-associated variants improves polygenic risk prediction. The American Journal of Human Genetics. 109(6). 1055–1064. 13 indexed citations
5.
Aguirre, Matthew, Yosuke Tanigawa, Guhan Venkataraman, et al.. (2021). Polygenic risk modeling with latent trait-related genetic components. European Journal of Human Genetics. 29(7). 1071–1081. 7 indexed citations
6.
Venkataraman, Guhan, Christopher DeBoever, Yosuke Tanigawa, et al.. (2021). Bayesian model comparison for rare-variant association studies. The American Journal of Human Genetics. 108(12). 2354–2367. 5 indexed citations
7.
Aguirre, Matthew, et al.. (2021). Online distraction detection for naturalistic driving dataset using kinematic motion models and a multiple model algorithm. Transportation Research Part C Emerging Technologies. 130. 103317–103317. 11 indexed citations
8.
Qian, Junyang, Yosuke Tanigawa, Matthew Aguirre, et al.. (2020). A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genetics. 16(10). e1009141–e1009141. 67 indexed citations
9.
Tcheandjieu, Catherine, Matthew Aguirre, Stefan Gustafsson, et al.. (2020). A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. PLoS Genetics. 16(11). e1008802–e1008802. 8 indexed citations
10.
DeBoever, Christopher, Yosuke Tanigawa, Matthew Aguirre, et al.. (2020). Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. The American Journal of Human Genetics. 106(5). 611–622. 28 indexed citations
11.
Tanigawa, Yosuke, Jiehan Li, Johanne Marie Justesen, et al.. (2019). Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nature Communications. 10(1). 4064–4064. 39 indexed citations
12.
Aguirre, Matthew, Manuel A. Rivas, & James R. Priest. (2019). Phenome-wide Burden of Copy-Number Variation in the UK Biobank. The American Journal of Human Genetics. 105(2). 373–383. 40 indexed citations
13.
Ruderfer, Douglas M., Colin G. Walsh, Matthew Aguirre, et al.. (2019). Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. Molecular Psychiatry. 25(10). 2422–2430. 79 indexed citations
14.
Ruderfer, Douglas M., Colin G. Walsh, Matthew Aguirre, et al.. (2019). 62SIGNIFICANT SHARED HERITABILITY UNDERLIES SUICIDE ATTEMPT AND CLINICALLY PREDICTED PROBABILITY OF ATTEMPTING SUICIDE. European Neuropsychopharmacology. 29. S1102–S1102. 3 indexed citations
15.
McInnes, Gregory, Yosuke Tanigawa, Adam Lavertu, et al.. (2018). Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics. 35(14). 2495–2497. 44 indexed citations
16.
Tukiainen, Taru, Alexandra–Chloé Villani, Angela Yen, et al.. (2017). Landscape of X chromosome inactivation across human tissues. Nature. 550(7675). 244–248. 665 indexed citations breakdown →
17.
Leung, Alice, Matthew Aguirre, Jiawei Han, et al.. (2013). Social patterns: Community detection using behavior-generated network datasets. 82–89. 1 indexed citations
18.
Voss, Clare R., et al.. (2008). Boosting performance of weak MT engines automatically: using MT output to align segments & build statistical post-editors. 192–201. 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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