Nicholas E. Navin

21.1k total citations · 6 hit papers
74 papers, 10.5k citations indexed

About

Nicholas E. Navin is a scholar working on Cancer Research, Molecular Biology and Genetics. According to data from OpenAlex, Nicholas E. Navin has authored 74 papers receiving a total of 10.5k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Cancer Research, 49 papers in Molecular Biology and 14 papers in Genetics. Recurrent topics in Nicholas E. Navin's work include Cancer Genomics and Diagnostics (54 papers), Single-cell and spatial transcriptomics (36 papers) and Genomic variations and chromosomal abnormalities (12 papers). Nicholas E. Navin is often cited by papers focused on Cancer Genomics and Diagnostics (54 papers), Single-cell and spatial transcriptomics (36 papers) and Genomic variations and chromosomal abnormalities (12 papers). Nicholas E. Navin collaborates with scholars based in United States, Sweden and Germany. Nicholas E. Navin's co-authors include James Hicks, Yong Wang, Ruli Gao, Michael Wigler, Jennifer Troge, Emi Sei, Jude Kendall, Linda Rodgers, Alexander Davis and Ken Chen and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Nicholas E. Navin

73 papers receiving 10.3k citations

Hit Papers

Tumour evolution inferred... 2004 2026 2011 2018 2011 2004 2014 2018 2015 500 1000 1.5k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Nicholas E. Navin 6.6k 5.5k 2.8k 2.6k 1.1k 74 10.5k
Ronny Drapkin 8.4k 1.3× 3.0k 0.5× 3.3k 1.2× 1.7k 0.7× 1.1k 1.0× 175 14.3k
Suet‐Feung Chin 6.8k 1.0× 5.7k 1.1× 3.2k 1.2× 1.5k 0.6× 2.1k 2.0× 98 11.0k
Rameen Beroukhim 10.0k 1.5× 6.1k 1.1× 4.1k 1.5× 2.2k 0.9× 3.2k 3.0× 155 17.0k
Jane Bayani 5.7k 0.9× 3.2k 0.6× 4.8k 1.7× 997 0.4× 1.5k 1.4× 111 10.5k
Gregory J. Riggins 11.7k 1.8× 5.3k 1.0× 4.1k 1.5× 1.3k 0.5× 2.2k 2.0× 117 18.4k
John W.M. Martens 6.8k 1.0× 4.8k 0.9× 4.4k 1.6× 1.5k 0.6× 2.1k 1.9× 328 11.9k
Evelin Schröck 6.0k 0.9× 2.2k 0.4× 1.8k 0.6× 4.1k 1.6× 724 0.7× 179 10.2k
Dan Pinkel 4.9k 0.7× 2.2k 0.4× 3.5k 1.3× 3.2k 1.2× 737 0.7× 34 8.9k
David D.L. Bowtell 11.6k 1.8× 3.5k 0.6× 5.4k 1.9× 2.1k 0.8× 1.4k 1.3× 221 18.4k
Anne Kallioniemi 8.2k 1.2× 4.2k 0.8× 3.8k 1.4× 5.7k 2.2× 1.6k 1.5× 138 14.2k

Countries citing papers authored by Nicholas E. Navin

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas E. Navin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas E. Navin. 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 Nicholas E. Navin. The network helps show where Nicholas E. Navin may publish in the future.

Co-authorship network of co-authors of Nicholas E. Navin

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas E. Navin. A scholar is included among the top collaborators of Nicholas E. Navin 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 Nicholas E. Navin. Nicholas E. Navin 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.
Ayoub, Edward, Li Li, Muharrem Müftüoğlu, et al.. (2024). Single-Cell Multiomics Unveils Venetoclax-Resistant Monocytic Differentiation and Immune Evasion in TP53 Mutant AML Clones. Blood. 144(Supplement 1). 61–61. 2 indexed citations
2.
Wang, Jun, Xuesen Cheng, Kyung Yeon Han, et al.. (2023). Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nature Biotechnology. 42(5). 803–812. 19 indexed citations
3.
Ramesh, Naveen, Min Hu, Sayaka Hashimoto, et al.. (2022). A Decade's Experience in Pediatric Chromosomal Microarray Reveals Distinct Characteristics Across Ordering Specialties. Journal of Molecular Diagnostics. 24(9). 1031–1040. 3 indexed citations
4.
Schalck, Aislyn, Donastas Sakellariou-Thompson, Marie‐Andrée Forget, et al.. (2022). Single-Cell Sequencing Reveals Trajectory of Tumor-Infiltrating Lymphocyte States in Pancreatic Cancer. Cancer Discovery. 12(10). 2330–2349. 44 indexed citations
5.
Kaufmann, Tom L., Marina Petković, Thomas B.K. Watkins, et al.. (2022). MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution. Genome biology. 23(1). 241–241. 45 indexed citations
6.
Navin, Nicholas E., Orit Rozenblatt–Rosen, & Nancy R. Zhang. (2021). New frontiers in single-cell genomics. Genome Research. 31(10). ix–x. 1 indexed citations
7.
Gao, Ruli, Shanshan Bai, Ying C. Henderson, et al.. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature Biotechnology. 39(5). 599–608. 413 indexed citations breakdown →
8.
Malihi, Paymaneh D., Ryon P. Graf, Ángel Rodríguez, et al.. (2020). Single-Cell Circulating Tumor Cell Analysis Reveals Genomic Instability as a Distinctive Feature of Aggressive Prostate Cancer. Clinical Cancer Research. 26(15). 4143–4153. 61 indexed citations
9.
Lim, Bora, Yiyun Lin, & Nicholas E. Navin. (2020). Advancing Cancer Research and Medicine with Single-Cell Genomics. Cancer Cell. 37(4). 456–470. 183 indexed citations
10.
Zafar, Hamim, Nicholas E. Navin, Ken Chen, & Luay Nakhleh. (2019). SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Research. 29(11). 1847–1859. 68 indexed citations
11.
Davis, Alexander, Ruli Gao, & Nicholas E. Navin. (2019). SCOPIT: sample size calculations for single-cell sequencing experiments. BMC Bioinformatics. 20(1). 566–566. 33 indexed citations
12.
Casasent, Tod D., Aislyn Schalck, Ruli Gao, et al.. (2018). Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing. Cell. 172(1-2). 205–217.e12. 279 indexed citations
13.
Zhang, Yun, Shunbin Xiong, Bin Liu, et al.. (2018). Somatic Trp53 mutations differentially drive breast cancer and evolution of metastases. Nature Communications. 9(1). 3953–3953. 45 indexed citations
14.
Leung, Marco L., Alexander Davis, Ruli Gao, et al.. (2017). Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Research. 27(8). 1287–1299. 147 indexed citations
15.
Navin, Nicholas E.. (2015). The first five years of single-cell cancer genomics and beyond. Genome Research. 25(10). 1499–1507. 254 indexed citations
16.
Navin, Nicholas E.. (2014). Cancer genomics: one cell at a time. Genome biology. 15(8). 452–452. 220 indexed citations
17.
Decker, David A., et al.. (2011). Toward an Integrated Knowledge Environment to Support Modern Oncology. The Cancer Journal. 17(4). 257–263. 5 indexed citations
18.
Navin, Nicholas E. & James Hicks. (2010). Tracing the tumor lineage. Molecular Oncology. 4(3). 267–283. 106 indexed citations
19.
Navin, Nicholas E., Alexander Krasnitz, Linda Rodgers, et al.. (2009). Inferring tumor progression from genomic heterogeneity. Genome Research. 20(1). 68–80. 367 indexed citations
20.
Hicks, James, Lakshmi Muthuswamy, Alexander Krasnitz, et al.. (2005). High-Resolution ROMA CGH and FISH Analysis of Aneuploid and Diploid Breast Tumors. Cold Spring Harbor Symposia on Quantitative Biology. 70(0). 51–63. 10 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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