Alexis Battle

24.9k total citations · 1 hit paper
56 papers, 2.9k citations indexed

About

Alexis Battle is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Alexis Battle has authored 56 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 20 papers in Genetics and 5 papers in Cancer Research. Recurrent topics in Alexis Battle's work include Genetic Associations and Epidemiology (17 papers), Bioinformatics and Genomic Networks (12 papers) and Genomics and Chromatin Dynamics (11 papers). Alexis Battle is often cited by papers focused on Genetic Associations and Epidemiology (17 papers), Bioinformatics and Genomic Networks (12 papers) and Genomics and Chromatin Dynamics (11 papers). Alexis Battle collaborates with scholars based in United States, Canada and Ireland. Alexis Battle's co-authors include Yoav Gilad, Stephen B. Montgomery, Daphne Koller, Sara Mostafavi, Jonathan K. Pritchard, Joe R. Davis, Benjamin D. Umans, Sidney H. Wang, Zia Khan and Michael Ford and has published in prestigious journals such as Nature, Science and Circulation.

In The Last Decade

Alexis Battle

53 papers receiving 2.9k citations

Hit Papers

The impact of structural variation on human gene expression 2017 2026 2020 2023 2017 50 100 150 200 250
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. The impact of structural variation on human gene expression
    2017 264 citations Colby Chiang, Alexandra J. Scott et al. Nature Genetics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexis Battle United States 27 2.0k 1.2k 323 249 161 56 2.9k
Tony Burdett United Kingdom 13 2.5k 1.2× 1.8k 1.5× 477 1.5× 296 1.2× 126 0.8× 32 4.2k
Ryan K. C. Yuen Canada 27 2.2k 1.1× 1.4k 1.2× 395 1.2× 151 0.6× 177 1.1× 49 3.7k
Joannella Morales United States 8 2.1k 1.0× 1.9k 1.6× 393 1.2× 252 1.0× 98 0.6× 12 3.6k
Danielle Welter Luxembourg 6 1.8k 0.9× 1.6k 1.4× 368 1.1× 238 1.0× 66 0.4× 12 3.2k
Chandra L. Theesfeld United States 21 2.6k 1.3× 515 0.4× 260 0.8× 110 0.4× 202 1.3× 34 3.3k
Manuel A. Rivas United States 28 1.3k 0.6× 1.6k 1.3× 237 0.7× 140 0.6× 89 0.6× 67 2.7k
Peggy Hall United States 5 1.8k 0.9× 1.6k 1.4× 370 1.1× 238 1.0× 68 0.4× 8 3.3k
Kenneth Katz United States 14 2.1k 1.0× 997 0.8× 396 1.2× 275 1.1× 136 0.8× 19 3.2k
Farhad Hormozdiari United States 27 1.8k 0.9× 1.9k 1.6× 276 0.9× 102 0.4× 166 1.0× 56 3.1k
Eurie L. Hong United States 14 1.8k 0.9× 873 0.7× 360 1.1× 224 0.9× 118 0.7× 22 2.8k

Countries citing papers authored by Alexis Battle

Since Specialization
Citations

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

Fields of papers citing papers by Alexis Battle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexis Battle

This figure shows the co-authorship network connecting the top 25 collaborators of Alexis Battle. A scholar is included among the top collaborators of Alexis Battle 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 Alexis Battle. Alexis Battle 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.
Kernfeld, Eric, et al.. (2025). A comparison of computational methods for expression forecasting. Genome biology. 26(1). 388–388. 3 indexed citations
2.
Jensen, Tanner, Bohan Ni, Chloe M. Reuter, et al.. (2025). Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease. Genome Research. 35(4). 914–928. 2 indexed citations
3.
Duong, ThuyVy, Thomas R. Austin, Jennifer A. Brody, et al.. (2024). Circulating Blood Plasma Profiling Reveals Proteomic Signature and a Causal Role for SVEP1 in Sudden Cardiac Death. Circulation Genomic and Precision Medicine. 17(5). e004494–e004494.
4.
Strober, Benjamin J., Guanghao Qi, M. Grace Gordon, et al.. (2024). SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models. Genome biology. 25(1). 28–28. 4 indexed citations
5.
Qi, Guanghao, Surya B. Chhetri, Debashree Ray, et al.. (2024). Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nature Communications. 15(1). 6985–6985. 3 indexed citations
6.
Aguet, François, Kaur Alasoo, Yang Li, et al.. (2023). Molecular quantitative trait loci. Nature Reviews Methods Primers. 3(1). 49 indexed citations
7.
Qi, Guanghao, et al.. (2023). Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects. Nature Communications. 14(1). 6317–6317. 12 indexed citations
8.
Kirsche, Melanie, Rachel M. Sherman, Bohan Ni, et al.. (2023). Jasmine and Iris: population-scale structural variant comparison and analysis. Nature Methods. 20(3). 408–417. 44 indexed citations
9.
Elorbany, Reem, Katherine Rhodes, Benjamin J. Strober, et al.. (2022). Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genetics. 18(1). e1009666–e1009666. 36 indexed citations
10.
Arvanitis, Marios, et al.. (2022). Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity. The American Journal of Human Genetics. 109(2). 223–239. 42 indexed citations
11.
Dutta, Diptavo, Yuan He, Ashis Saha, et al.. (2022). Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood. Nature Communications. 13(1). 4323–4323. 12 indexed citations
12.
He, Yuan, Surya B. Chhetri, Marios Arvanitis, et al.. (2020). sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome biology. 21(1). 235–235. 11 indexed citations
13.
Kammers, Kai, Margaret A. Taub, Benjamin A.T. Rodriguez, et al.. (2020). Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing. Blood. 137(7). 959–968. 13 indexed citations
14.
Strober, Benjamin J., et al.. (2019). Dynamic genetic regulation of gene expression during cellular differentiation. Science. 364(6447). 1287–1290. 108 indexed citations
15.
Ratnapriya, Rinki, Olukayode Sosina, Margaret R. Starostik, et al.. (2019). Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nature Genetics. 51(4). 606–610. 175 indexed citations
16.
Chiang, Colby, Alexandra J. Scott, Joe R. Davis, et al.. (2017). The impact of structural variation on human gene expression. Nature Genetics. 49(5). 692–699. 264 indexed citations breakdown →
17.
Knowles, David A., Joe R. Davis, Anil Raj, et al.. (2017). Allele-specific expression reveals interactions between genetic variation and environment. Nature Methods. 14(7). 699–702. 88 indexed citations
18.
Saha, Ashis, Yungil Kim, Ariel DH Gewirtz, et al.. (2017). Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Research. 27(11). 1843–1858. 102 indexed citations
19.
Sharon, Eilon, Leah V. Sibener, Alexis Battle, et al.. (2016). Genetic variation in MHC proteins is associated with T cell receptor expression biases. Nature Genetics. 48(9). 995–1002. 102 indexed citations
20.
Battle, Alexis, et al.. (2016). Standardized Photometric Calibrations for Panchromatic SSA Sensors. Advanced Maui Optical and Space Surveillance Technologies Conference. 51.

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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