Jeffrey Schubert

458 total citations
9 papers, 188 citations indexed

About

Jeffrey Schubert is a scholar working on Genetics, Molecular Biology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Jeffrey Schubert has authored 9 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Genetics, 3 papers in Molecular Biology and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Jeffrey Schubert's work include Genomics and Rare Diseases (2 papers), Aortic Disease and Treatment Approaches (2 papers) and Cellular transport and secretion (2 papers). Jeffrey Schubert is often cited by papers focused on Genomics and Rare Diseases (2 papers), Aortic Disease and Treatment Approaches (2 papers) and Cellular transport and secretion (2 papers). Jeffrey Schubert collaborates with scholars based in United States, Australia and Singapore. Jeffrey Schubert's co-authors include Marilyn M. Li, Jinhua Wu, Yiming Zhong, Feng Xu, Stephanie M. Ware, Benjamin J. Landis, Amy Shikany, Robert B. Hinton, Erin M. Miller and Muhammad Tariq and has published in prestigious journals such as Circulation, Human Mutation and Biologia Plantarum.

In The Last Decade

Jeffrey Schubert

9 papers receiving 181 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jeffrey Schubert United States	5	83	47	40	39	30	9	188
Z. Xiong United States	9	112 1.3×	25 0.5×	28 0.7×	17 0.4×	8 0.3×	17	262
James A. Browne United States	13	193 2.3×	64 1.4×	51 1.3×	76 1.9×	9 0.3×	21	357
Magdalena M. Żak United States	8	103 1.2×	24 0.5×	16 0.4×	14 0.4×	29 1.0×	16	164
Esther Yoon United States	9	89 1.1×	59 1.3×	46 1.1×	14 0.4×	38 1.3×	25	235
Marfa Blanter Belgium	8	67 0.8×	30 0.6×	26 0.7×	57 1.5×	6 0.2×	12	280
Ellen Thomas United Kingdom	6	73 0.9×	14 0.3×	26 0.7×	57 1.5×	9 0.3×	12	174
Nadja Sachs Germany	7	96 1.2×	28 0.6×	33 0.8×	11 0.3×	22 0.7×	19	199
Huaming Sun United States	6	134 1.6×	29 0.6×	54 1.4×	42 1.1×	9 0.3×	8	236
Yichun Xiong China	5	225 2.7×	40 0.9×	54 1.4×	46 1.2×	7 0.2×	7	271

Countries citing papers authored by Jeffrey Schubert

Since Specialization

Citations

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

Fields of papers citing papers by Jeffrey Schubert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeffrey Schubert

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

All Works

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9 of 9 papers shown

Schubert, Jeffrey, Jinhua Wu, Marilyn M. Li, & Kajia Cao. (2022). Best Practice for Clinical Somatic Variant Interpretation and Reporting. Clinics in Laboratory Medicine. 42(3). 423–434. 1 indexed citations

Zhong, Yiming, Jeffrey Schubert, Jinhua Wu, et al.. (2020). A germline PALB2 pathogenic variant identified in a pediatric high-grade glioma. Molecular Case Studies. 6(4). a005397–a005397. 2 indexed citations

Zhong, Yiming, Feng Xu, Jinhua Wu, Jeffrey Schubert, & Marilyn M. Li. (2020). Application of Next Generation Sequencing in Laboratory Medicine. Annals of Laboratory Medicine. 41(1). 25–43. 121 indexed citations

Schubert, Jeffrey, Muhammad Tariq, Gabrielle C. Geddes, et al.. (2018). Novel pathogenic variants in filamin C identified in pediatric restrictive cardiomyopathy. Human Mutation. 39(12). 2083–2096. 18 indexed citations

Landis, Benjamin J., Jeffrey Schubert, Dongbing Lai, et al.. (2017). Exome Sequencing Identifies Candidate Genetic Modifiers of Syndromic and Familial Thoracic Aortic Aneurysm Severity. Journal of Cardiovascular Translational Research. 10(4). 423–432. 21 indexed citations

Schubert, Jeffrey, Benjamin J. Landis, Amy Shikany, Robert B. Hinton, & Stephanie M. Ware. (2016). Clinically relevant variants identified in thoracic aortic aneurysm patients by research exome sequencing. American Journal of Medical Genetics Part A. 170(5). 1288–1294. 15 indexed citations

Ware, Stephanie M., Steven E. Lipshultz, Steven D. Colan, et al.. (2015). Abstract 16468: Results of Research Genetic Testing in Pediatric Cardiomyopathy Patients Justify Broader Clinical Genetic Testing. Circulation. 132(suppl_3). 1 indexed citations

Chang, Chan Fong, et al.. (2004). Expression and purification of the recombinant His-tagged GST-CD38 fusion protein using the baculovirus/insect cell expression system. Protein Expression and Purification. 40(2). 396–403. 6 indexed citations

Matoušek, Jaroslav, et al.. (1991). An immunochemical testing of pathophysiological reactions of several PSTVinfected tomato (Lycopersicon esculentum L.) cultivars. Biologia Plantarum. 33(5). 3 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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