Changgee Chang

683 total citations
23 papers, 272 citations indexed

About

Changgee Chang is a scholar working on Molecular Biology, Statistics and Probability and Artificial Intelligence. According to data from OpenAlex, Changgee Chang has authored 23 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 7 papers in Statistics and Probability and 5 papers in Artificial Intelligence. Recurrent topics in Changgee Chang's work include Gene expression and cancer classification (13 papers), Bioinformatics and Genomic Networks (12 papers) and Statistical Methods and Inference (7 papers). Changgee Chang is often cited by papers focused on Gene expression and cancer classification (13 papers), Bioinformatics and Genomic Networks (12 papers) and Statistical Methods and Inference (7 papers). Changgee Chang collaborates with scholars based in United States, Switzerland and Brazil. Changgee Chang's co-authors include Qi Long, Ruey S. Tsay, Suprateek Kundu, Xiaoqian Jiang, Alyssa M. Civantos, Antônio José Gonçalves, Yize Zhao, Karthik Rajasekaran, Emily Getzen and Ziyi Li and has published in prestigious journals such as Nature Communications, Journal of the American Statistical Association and Scientific Reports.

In The Last Decade

Changgee Chang

23 papers receiving 267 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Changgee Chang United States 8 80 60 58 42 41 23 272
Aaron Fisher United States 7 47 0.6× 39 0.7× 101 1.7× 6 0.1× 12 0.3× 13 357
Emily Slade United States 9 42 0.5× 15 0.3× 34 0.6× 23 0.5× 24 0.6× 42 315
Leah R. Jager United States 10 53 0.7× 115 1.9× 51 0.9× 5 0.1× 10 0.2× 22 413
Hansi Zhang United States 12 91 1.1× 20 0.3× 143 2.5× 27 0.6× 7 0.2× 28 379
Yaara Goldschmidt Israel 9 54 0.7× 15 0.3× 133 2.3× 17 0.4× 11 0.3× 15 339
Huazhen Lin China 12 81 1.0× 320 5.3× 105 1.8× 9 0.2× 10 0.2× 60 540
Heidi Seibold Germany 11 29 0.4× 90 1.5× 62 1.1× 8 0.2× 7 0.2× 21 293
Alice Tang United States 8 110 1.4× 8 0.1× 33 0.6× 29 0.7× 27 0.7× 23 301
Murat Sariyar Germany 10 35 0.4× 12 0.2× 57 1.0× 25 0.6× 9 0.2× 26 244
Peisong Han United States 14 26 0.3× 466 7.8× 67 1.2× 22 0.5× 48 1.2× 53 703

Countries citing papers authored by Changgee Chang

Since Specialization
Citations

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

Fields of papers citing papers by Changgee Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changgee Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Changgee Chang. A scholar is included among the top collaborators of Changgee Chang 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 Changgee Chang. Changgee Chang 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.
Bao, Jingxuan, Junhao Wen, Changgee Chang, et al.. (2025). A genetically informed brain atlas for enhancing brain imaging genomics. Nature Communications. 16(1). 3524–3524. 2 indexed citations
2.
Chang, Changgee, et al.. (2024). Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data. Biometrics. 80(1). 1 indexed citations
3.
Chang, Changgee, et al.. (2024). Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors. Biometrics. 80(1). 1 indexed citations
4.
5.
Chang, Changgee, et al.. (2023). Robust knowledge-guided biclustering for multi-omics data. Briefings in Bioinformatics. 25(1). 2 indexed citations
6.
Zhao, Yize, Changgee Chang, Jingwen Zhang, & Zhengwu Zhang. (2022). Genetic Underpinnings of Brain Structural Connectome for Young Adults. Journal of the American Statistical Association. 118(543). 1473–1487. 2 indexed citations
7.
Chang, Changgee, et al.. (2022). Integrative learning of structuredhigh‐dimensionaldata from multiple datasets. Statistical Analysis and Data Mining The ASA Data Science Journal. 16(2). 120–134. 1 indexed citations
8.
Manatunga, Amita K., et al.. (2021). A Bayesian multiple imputation approach to bivariate functional data with missing components. Statistics in Medicine. 40(22). 4772–4793. 3 indexed citations
9.
Zhao, Yize, et al.. (2021). Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data. Scientific Reports. 11(1). 5146–5146. 7 indexed citations
10.
Chang, Changgee, et al.. (2021). Accounting for technical noise in Bayesian graphical models of single-cell RNA-sequencing data. Biostatistics. 24(1). 161–176. 1 indexed citations
11.
Civantos, Alyssa M., Antônio José Gonçalves, Emily Getzen, et al.. (2020). Mental health among head and neck surgeons in Brazil during the COVID-19 pandemic: A national study. American Journal of Otolaryngology. 41(6). 102694–102694. 48 indexed citations
12.
Chang, Changgee, et al.. (2020). Multiple imputation for analysis of incomplete data in distributed health data networks. Nature Communications. 11(1). 5467–5467. 29 indexed citations
13.
Chang, Changgee, et al.. (2019). Knowledge-Guided Biclustering via Sparse Variational EM Algorithm. PubMed. 4. 25–32. 2 indexed citations
14.
Chang, Changgee, et al.. (2019). Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features. PubMed. 2019. 4874–4882. 5 indexed citations
15.
Chang, Changgee, et al.. (2018). Knowledge-Guided Bayesian Support Vector Machine for High-Dimensional Data with Application to Analysis of Genomics Data. PubMed. 2018. 1484–1493. 7 indexed citations
16.
Chang, Changgee, et al.. (2018). Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data. PubMed. 2018. 109–119. 9 indexed citations
17.
Li, Ziyi, Changgee Chang, Suprateek Kundu, & Qi Long. (2018). Bayesian generalized biclustering analysis via adaptive structured shrinkage. Biostatistics. 21(3). 610–624. 11 indexed citations
18.
Chang, Changgee, Suprateek Kundu, & Qi Long. (2018). Scalable Bayesian Variable Selection for Structured High-Dimensional Data. Biometrics. 74(4). 1372–1382. 21 indexed citations
19.
Chang, Changgee, et al.. (2016). Multiple Imputation for General Missing Data Patterns in the Presence of High-dimensional Data. Scientific Reports. 6(1). 21689–21689. 73 indexed citations
20.
Chang, Changgee & Ruey S. Tsay. (2010). Estimation of covariance matrix via the sparse Cholesky factor with lasso. Journal of Statistical Planning and Inference. 140(12). 3858–3873. 25 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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