Nae-Chyun Chen

4.1k total citations
14 papers, 170 citations indexed

About

Nae-Chyun Chen is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Nae-Chyun Chen has authored 14 papers receiving a total of 170 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 5 papers in Genetics and 5 papers in Plant Science. Recurrent topics in Nae-Chyun Chen's work include Genomics and Phylogenetic Studies (10 papers), Chromosomal and Genetic Variations (3 papers) and Algorithms and Data Compression (3 papers). Nae-Chyun Chen is often cited by papers focused on Genomics and Phylogenetic Studies (10 papers), Chromosomal and Genetic Variations (3 papers) and Algorithms and Data Compression (3 papers). Nae-Chyun Chen collaborates with scholars based in United States, Taiwan and South Korea. Nae-Chyun Chen's co-authors include Ben Langmead, Brad Solomon, Yi-Chang Lu, Yucheng Li, Andrew Carroll, Pi-Chuan Chang, Alexey Kolesnikov, Taedong Yun, Sidharth Goel and Adam M. Phillippy and has published in prestigious journals such as Bioinformatics, Nature Methods and Genome biology.

In The Last Decade

Nae-Chyun Chen

13 papers receiving 169 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nae-Chyun Chen United States 8 129 70 30 28 16 14 170
Philippe Gambette France 8 131 1.0× 74 1.1× 21 0.7× 19 0.7× 7 0.4× 23 197
Ydo Wexler Israel 8 188 1.5× 66 0.9× 23 0.8× 48 1.7× 9 0.6× 20 255
Alexander Wait Zaranek United States 6 168 1.3× 76 1.1× 16 0.5× 25 0.9× 37 2.3× 9 258
Christopher R. John United Kingdom 4 188 1.5× 56 0.8× 64 2.1× 24 0.9× 25 1.6× 6 301
Shitij Bhargava United States 3 103 0.8× 22 0.3× 17 0.6× 24 0.9× 13 0.8× 5 163
Raluca Uricaru France 6 217 1.7× 37 0.5× 30 1.0× 59 2.1× 35 2.2× 13 281
Ruoyu Chen China 10 289 2.2× 24 0.3× 16 0.5× 23 0.8× 24 1.5× 18 358
E. A. Ananko Russia 10 232 1.8× 41 0.6× 21 0.7× 12 0.4× 15 0.9× 24 278
Fatemeh Almodaresi United States 7 201 1.6× 26 0.4× 32 1.1× 69 2.5× 19 1.2× 11 246
Derek E. Kelly United States 6 91 0.7× 78 1.1× 22 0.7× 13 0.5× 27 1.7× 7 202

Countries citing papers authored by Nae-Chyun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Nae-Chyun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nae-Chyun Chen. 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 Nae-Chyun Chen. The network helps show where Nae-Chyun Chen may publish in the future.

Co-authorship network of co-authors of Nae-Chyun Chen

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

All Works

14 of 14 papers shown
1.
Wenz, Brandon M., Yuan He, Nae-Chyun Chen, et al.. (2025). Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms. Genome biology. 26(1). 81–81.
2.
Chen, Nae-Chyun, et al.. (2024). Measuring, visualizing, and diagnosing reference bias with biastools. Genome biology. 25(1). 101–101. 4 indexed citations
3.
Yang, Xiangyu, Yawen Zou, Shilong Zhang, et al.. (2023). Characterization of large-scale genomic differences in the first complete human genome. Genome biology. 24(1). 157–157. 10 indexed citations
4.
Chen, Nae-Chyun, Alexey Kolesnikov, Sidharth Goel, et al.. (2023). Improving variant calling using population data and deep learning. BMC Bioinformatics. 24(1). 197–197. 10 indexed citations
5.
Chen, Nae-Chyun, Luis F. Paulin, Fritz J. Sedlazeck, et al.. (2023). Improved sequence mapping using a complete reference genome and lift-over. Nature Methods. 21(1). 41–49. 8 indexed citations
6.
Chen, Nae-Chyun, Chia‐Lang Hsu, Jacob Shujui Hsu, et al.. (2022). Profiling genes encoding the adaptive immune receptor repertoire with gAIRR Suite. Frontiers in Immunology. 13. 922513–922513. 8 indexed citations
7.
Chen, Nae-Chyun, et al.. (2021). LevioSAM: fast lift-over of variant-aware reference alignments. Bioinformatics. 37(22). 4243–4245. 8 indexed citations
8.
Chen, Nae-Chyun, et al.. (2021). Reference flow: reducing reference bias using multiple population genomes. Genome biology. 22(1). 8–8. 49 indexed citations
9.
Chen, Nae-Chyun, et al.. (2018). FORGe: prioritizing variants for graph genomes. Genome biology. 19(1). 220–220. 48 indexed citations
10.
Chen, Nae-Chyun, Yucheng Li, & Yi-Chang Lu. (2018). A Memory-Efficient FM-Index Constructor for Next-Generation Sequencing Applications on FPGAs. 1–4. 2 indexed citations
11.
Li, Yucheng, et al.. (2018). Adaptively Banded Smith-Waterman Algorithm for Long Reads and Its Hardware Accelerator. 1–9. 19 indexed citations
12.
Chen, Yi‐Hsiang, et al.. (2016). Queue-based segmentation algorithm for refining depth maps in light field camera applications. 1–2. 1 indexed citations
13.
14.

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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