Chengzhong Ye

1.6k total citations · 1 hit paper
7 papers, 263 citations indexed

About

Chengzhong Ye is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Chengzhong Ye has authored 7 papers receiving a total of 263 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Genetics and 1 paper in Artificial Intelligence. Recurrent topics in Chengzhong Ye's work include Genetic Associations and Epidemiology (3 papers), Single-cell and spatial transcriptomics (3 papers) and Genomics and Phylogenetic Studies (3 papers). Chengzhong Ye is often cited by papers focused on Genetic Associations and Epidemiology (3 papers), Single-cell and spatial transcriptomics (3 papers) and Genomics and Phylogenetic Studies (3 papers). Chengzhong Ye collaborates with scholars based in United States, China and Australia. Chengzhong Ye's co-authors include Jingshu Wang, Zilu Zhou, Nancy R. Zhang, Divyansh Agarwal, Gang Hu, Mo Huang, Yun S. Song, Terence P. Speed, Agus Salim and Nilah M. Ioannidis and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Bioinformatics.

In The Last Decade

Chengzhong Ye

7 papers receiving 261 citations

Hit Papers

1 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.

Genomic language models: opportunities and challenges

2025 21 citations Chengzhong Ye, Jianan Canal Li et al. Trends in Genetics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chengzhong Ye United States	6	212	55	43	34	24	7	263
Michael Vinyard United States	7	316 1.5×	58 1.1×	47 1.1×	23 0.7×	28 1.2×	9	350
Shaliu Fu China	10	170 0.8×	48 0.9×	19 0.4×	14 0.4×	29 1.2×	15	228
Ron Zeira Israel	7	221 1.0×	82 1.5×	26 0.6×	41 1.2×	24 1.0×	13	241
Hussein Mohsen United States	6	240 1.1×	85 1.5×	57 1.3×	40 1.2×	20 0.8×	16	325
Nika Iremadze United States	4	258 1.2×	33 0.6×	41 1.0×	36 1.1×	21 0.9×	4	285
Dongyuan Song United States	7	292 1.4×	81 1.5×	36 0.8×	14 0.4×	36 1.5×	16	330
Amogh P. Jalihal United States	4	404 1.9×	28 0.5×	45 1.0×	17 0.5×	17 0.7×	8	429
Siyuan Chen Saudi Arabia	7	160 0.8×	44 0.8×	26 0.6×	12 0.4×	33 1.4×	11	235
Matthew Amodio United States	5	199 0.9×	58 1.1×	67 1.6×	20 0.6×	40 1.7×	12	268

Countries citing papers authored by Chengzhong Ye

Since Specialization

Citations

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

Fields of papers citing papers by Chengzhong Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengzhong Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Chengzhong Ye. A scholar is included among the top collaborators of Chengzhong Ye 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 Chengzhong Ye. Chengzhong Ye 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:

7 of 7 papers shown

1.

Ye, Chengzhong, et al.. (2025). A DNA language model based on multispecies alignment predicts the effects of genome-wide variants. Nature Biotechnology. 43(12). 1960–1965. 15 indexed citations

2.

Ye, Chengzhong, et al.. (2025). Genomic language models: opportunities and challenges. Trends in Genetics. 41(4). 286–302. 21 indexed citations breakdown →

3.

Kumbier, Karl, Aldo Córdova‐Palomera, Matthew Aguirre, et al.. (2024). Learning epistatic polygenic phenotypes with Boolean interactions. PLoS ONE. 19(4). e0298906–e0298906. 2 indexed citations

4.

Ye, Chengzhong, et al.. (2023). Cross-protein transfer learning substantially improves disease variant prediction. Genome biology. 24(1). 182–182. 31 indexed citations

5.

Zhou, Zilu, Chengzhong Ye, Jingshu Wang, & Nancy R. Zhang. (2020). Surface protein imputation from single cell transcriptomes by deep neural networks. Nature Communications. 11(1). 651–651. 49 indexed citations

6.

Ye, Chengzhong, Terence P. Speed, & Agus Salim. (2019). DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data. Bioinformatics. 35(24). 5155–5162. 27 indexed citations

7.

Wang, Jingshu, Divyansh Agarwal, Mo Huang, et al.. (2019). Data denoising with transfer learning in single-cell transcriptomics. Nature Methods. 16(9). 875–878. 118 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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