Clara Toma

1.7k total citations · 1 hit paper
8 papers, 1.1k citations indexed

About

Clara Toma is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Clara Toma has authored 8 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology, 5 papers in Molecular Biology and 1 paper in Oncology. Recurrent topics in Clara Toma's work include T-cell and B-cell Immunology (7 papers), Immune Cell Function and Interaction (6 papers) and Immunotherapy and Immune Responses (3 papers). Clara Toma is often cited by papers focused on T-cell and B-cell Immunology (7 papers), Immune Cell Function and Interaction (6 papers) and Immunotherapy and Immune Responses (3 papers). Clara Toma collaborates with scholars based in United States, Australia and Netherlands. Clara Toma's co-authors include Ananda W. Goldrath, J. Justin Milner, Kyla Omilusik, Bingfei Yu, Matthew E. Pipkin, Shane Crotty, John T. Chang, Kai Zhang, Toan Nguyen and Anthony T. Phan and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Immunity.

In The Last Decade

Clara Toma

8 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours

2017 446 citations J. Justin Milner, Clara Toma et al. Nature profile →

Peers

Clara Toma

IMMUN

ONCOL

MB

EPIDE

CR

J. Adam Best United States

Simone L. Park Australia

Renaud Lesourne France

Florent Carrette France

Jae-Ho Cho South Korea

Maura Crowley United States

Jun Tsukada Japan

Heba Nowyhed United States

Alejandra Mendoza United States

Georgina H. Cornish United Kingdom

J. Adam Best United States

Clara Toma

1.1k ×1.3

IMMUN

322 ×0.9

ONCOL

235 ×0.8

MB

82 ×1.5

EPIDE

54 ×1.4

CR

Citations per year, relative to Clara Toma Clara Toma (= 1×) peers J. Adam Best

Countries citing papers authored by Clara Toma

Since Specialization

Citations

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

Fields of papers citing papers by Clara Toma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Clara Toma

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Quon, Sara, Bingfei Yu, Brendan E. Russ, et al.. (2023). DNA architectural protein CTCF facilitates subset-specific chromatin interactions to limit the formation of memory CD8+ T cells. Immunity. 56(5). 959–978.e10. 12 indexed citations

2.

Omilusik, Kyla, Clara Toma, Nadia S. Kurd, et al.. (2022). Systems-level identification of key transcription factors in immune cell specification. PLoS Computational Biology. 18(9). e1010116–e1010116. 9 indexed citations

3.

Crowl, John T., Maximilian Heeg, Amir Ferry, et al.. (2022). Tissue-resident memory CD8+ T cells possess unique transcriptional, epigenetic and functional adaptations to different tissue environments. Nature Immunology. 23(7). 1121–1131. 141 indexed citations

4.

Milner, J. Justin, Kyla Omilusik, Miguel Reina‐Campos, et al.. (2020). Delineation of a molecularly distinct terminally differentiated memory CD8 T cell population. Proceedings of the National Academy of Sciences. 117(41). 25667–25678. 71 indexed citations

5.

Milner, J. Justin, Clara Toma, Zhaoren He, et al.. (2020). Heterogenous Populations of Tissue-Resident CD8+ T Cells Are Generated in Response to Infection and Malignancy. Immunity. 52(5). 808–824.e7. 174 indexed citations

6.

Yu, Bingfei, Kai Zhang, J. Justin Milner, et al.. (2017). Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation. Nature Immunology. 18(5). 573–582. 179 indexed citations

7.

Milner, J. Justin, Clara Toma, Bingfei Yu, et al.. (2017). Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature. 552(7684). 253–257. 446 indexed citations breakdown →

8.

Giardoglou, Tota, David R. Jones, Kathy A. Gelato, et al.. (2015). The Basal Transcription Complex Component TAF3 Transduces Changes in Nuclear Phosphoinositides into Transcriptional Output. Molecular Cell. 58(3). 453–467. 55 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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