Connor A. Jacobson

3.1k total citations
8 papers, 312 citations indexed

About

Connor A. Jacobson is a scholar working on Molecular Biology, Biophysics and Immunology. According to data from OpenAlex, Connor A. Jacobson has authored 8 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Biophysics and 3 papers in Immunology. Recurrent topics in Connor A. Jacobson's work include Single-cell and spatial transcriptomics (5 papers), CAR-T cell therapy research (2 papers) and Immunotherapy and Immune Responses (2 papers). Connor A. Jacobson is often cited by papers focused on Single-cell and spatial transcriptomics (5 papers), CAR-T cell therapy research (2 papers) and Immunotherapy and Immune Responses (2 papers). Connor A. Jacobson collaborates with scholars based in United States, Israel and Finland. Connor A. Jacobson's co-authors include Peter K. Sorger, Zoltan Maliga, Sandro Santagata, Clarence Yapp, Tuulia Vallius, Alyce A. Chen, Gëorge F. Murphy, Yu‐An Chen, Brian Quattrochi and Christine G. Lian and has published in prestigious journals such as Science, Nature Communications and Cancer Discovery.

In The Last Decade

Connor A. Jacobson

8 papers receiving 309 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Connor A. Jacobson United States	7	178	133	105	72	48	8	312
Jimin Guo Canada	9	411 2.3×	70 0.5×	45 0.4×	207 2.9×	16 0.3×	13	504
Benedict Anchang United States	7	253 1.4×	122 0.9×	55 0.5×	95 1.3×	51 1.1×	18	378
Laura Ferrer‐Font New Zealand	9	209 1.2×	40 0.3×	120 1.1×	21 0.3×	55 1.1×	23	338
Satoi Nagasawa Japan	9	280 1.6×	77 0.6×	40 0.4×	117 1.6×	17 0.4×	16	379
Maximillian Hafer Germany	7	135 0.8×	97 0.7×	106 1.0×	11 0.2×	18 0.4×	9	354
Irina Heid Germany	10	105 0.6×	155 1.2×	25 0.2×	49 0.7×	17 0.4×	19	334
Sunil R. Lakhani Australia	8	200 1.1×	63 0.5×	40 0.4×	84 1.2×	26 0.5×	14	306
Giulia Franciosa Denmark	11	252 1.4×	82 0.6×	44 0.4×	37 0.5×	5 0.1×	18	341
Maddalena M. Bolognesi Italy	11	210 1.2×	96 0.7×	75 0.7×	32 0.4×	68 1.4×	27	362
Erica S. Savig United States	5	408 2.3×	89 0.7×	147 1.4×	70 1.0×	129 2.7×	5	545

Countries citing papers authored by Connor A. Jacobson

Since Specialization

Citations

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

Fields of papers citing papers by Connor A. Jacobson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Connor A. Jacobson. 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 Connor A. Jacobson. The network helps show where Connor A. Jacobson may publish in the future.

Co-authorship network of co-authors of Connor A. Jacobson

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

All Works

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8 of 8 papers shown

1.

Godfrey, James, Xiufen Chen, Nicole Sunseri, et al.. (2023). TIGIT is a key inhibitory checkpoint receptor in lymphoma. Journal for ImmunoTherapy of Cancer. 11(6). e006582–e006582. 13 indexed citations

2.

Hodis, Eran, Elena Torlai Triglia, John Kwon, et al.. (2022). Stepwise-edited, human melanoma models reveal mutations’ effect on tumor and microenvironment. Science. 376(6592). eabi8175–eabi8175. 32 indexed citations

3.

Nirmal, Ajit J., Zoltan Maliga, Tuulia Vallius, et al.. (2022). The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution. Cancer Discovery. 12(6). 1518–1541. 119 indexed citations

4.

Casado, Julia, Connor A. Jacobson, Jia‐Ren Lin, et al.. (2022). Single-cell tumor-immune microenvironment of BRCA1/2 mutated high-grade serous ovarian cancer. Nature Communications. 13(1). 835–835. 50 indexed citations

5.

Yapp, Clarence, Edward Novikov, Won-Dong Jang, et al.. (2022). UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Communications Biology. 5(1). 1263–1263. 20 indexed citations

6.

Wu, Hua‐Jun, Daniel Temko, Zoltan Maliga, et al.. (2022). Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients. Cell Genomics. 2(8). 100165–100165. 27 indexed citations

7.

Nirmal, Ajit J., Zoltan Maliga, Tuulia Vallius, et al.. (2021). The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single Cell Resolution. SSRN Electronic Journal. 2 indexed citations

8.

Wang, Huan, Robert P. Sheehan, Adam C. Palmer, et al.. (2019). Adaptation of Human iPSC-Derived Cardiomyocytes to Tyrosine Kinase Inhibitors Reduces Acute Cardiotoxicity via Metabolic Reprogramming. Cell Systems. 8(5). 412–426.e7. 49 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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