Kenneth Chang

5.4k total citations · 1 hit paper
33 papers, 2.8k citations indexed

About

Kenneth Chang is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Kenneth Chang has authored 33 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 8 papers in Cancer Research and 4 papers in Oncology. Recurrent topics in Kenneth Chang's work include RNA Interference and Gene Delivery (13 papers), Advanced biosensing and bioanalysis techniques (10 papers) and CRISPR and Genetic Engineering (8 papers). Kenneth Chang is often cited by papers focused on RNA Interference and Gene Delivery (13 papers), Advanced biosensing and bioanalysis techniques (10 papers) and CRISPR and Genetic Engineering (8 papers). Kenneth Chang collaborates with scholars based in United States, Switzerland and United Kingdom. Kenneth Chang's co-authors include Gregory J. Hannon, Stephen J. Elledge, Michele A. Cleary, Patrick J. Paddison, José M. Silva, Joel S. Parker, Ravi Sachidanandam, Thomas F. Westbrook, Mamie Z. Li and Krista Marran and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Kenneth Chang

31 papers receiving 2.7k citations

Hit Papers

A resource for large-scale RNA-interference-based screens... 2004 2026 2011 2018 2004 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kenneth Chang United States 21 2.3k 560 414 371 238 33 2.8k
Pratyush Kumar Das United States 14 2.5k 1.1× 391 0.7× 223 0.5× 386 1.0× 185 0.8× 17 3.3k
Sandro J. de Souza Brazil 29 2.2k 1.0× 374 0.7× 224 0.5× 358 1.0× 107 0.4× 118 2.9k
Brian S. Roberts United States 21 2.4k 1.0× 1.1k 2.0× 315 0.8× 347 0.9× 147 0.6× 40 3.0k
Anthony J. Davis United States 27 2.0k 0.9× 388 0.7× 780 1.9× 164 0.4× 253 1.1× 62 2.8k
Nathan J. Bowen United States 26 1.9k 0.8× 435 0.8× 408 1.0× 260 0.7× 202 0.8× 49 2.8k
James Lee United States 25 1.9k 0.8× 274 0.5× 397 1.0× 410 1.1× 135 0.6× 40 2.4k
Thomas Royce United States 19 1.8k 0.8× 718 1.3× 198 0.5× 328 0.9× 98 0.4× 33 2.4k
Lev P. Ovchinnikov Russia 35 3.9k 1.7× 459 0.8× 496 1.2× 313 0.8× 125 0.5× 87 4.5k
Yegor Vassetzky France 35 2.7k 1.2× 310 0.6× 456 1.1× 404 1.1× 78 0.3× 160 3.4k
Kevin Blackburn United States 27 2.0k 0.9× 324 0.6× 412 1.0× 451 1.2× 147 0.6× 50 3.2k

Countries citing papers authored by Kenneth Chang

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kenneth Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth Chang. A scholar is included among the top collaborators of Kenneth 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 Kenneth Chang. Kenneth 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.
Alpsoy, Aktan, Jonathan J. Ipsaro, Damianos Skopelitis, et al.. (2025). Structural basis of DNA-dependent coactivator recruitment by the tuft cell master regulator POU2F3. Cell Reports. 44(11). 116572–116572.
2.
Cen, Yu‐Ke, Xiaoli Wu, Keith Rivera, et al.. (2024). PTPN23-dependent ESCRT machinery functions as a cell death checkpoint. Nature Communications. 15(1). 10364–10364. 1 indexed citations
3.
Chang, Kenneth, et al.. (2022). Induction of Filopodia During Cytomegalovirus Entry Into Human Iris Stromal Cells. Frontiers in Microbiology. 13. 834927–834927. 6 indexed citations
4.
Klingbeil, Olaf, Bin Lü, Caizhi Wu, et al.. (2022). BRD8 maintains glioblastoma by epigenetic reprogramming of the p53 network. Nature. 613(7942). 195–202. 46 indexed citations
5.
Roche, Benjamin, et al.. (2022). Dicer promotes genome stability via the bromodomain transcriptional co-activator BRD4. Nature Communications. 13(1). 1001–1001. 12 indexed citations
6.
Yang, Zhaolin, Xiaoli Wu, Yiliang Wei, et al.. (2021). Transcriptional Silencing of ALDH2 Confers a Dependency on Fanconi Anemia Proteins in Acute Myeloid Leukemia. Cancer Discovery. 11(9). 2300–2315. 20 indexed citations
7.
Gryder, Berkley E., Marco Wachtel, Kenneth Chang, et al.. (2020). Miswired Enhancer Logic Drives a Cancer of the Muscle Lineage. iScience. 23(5). 101103–101103. 28 indexed citations
8.
Chang, Kenneth, et al.. (2016). Filopodia and Viruses: An Analysis of Membrane Processes in Entry Mechanisms. Frontiers in Microbiology. 7. 300–300. 62 indexed citations
9.
Knott, Simon, Nicolas Erard, Kenneth Chang, et al.. (2014). A Computational Algorithm to Predict shRNA Potency. Molecular Cell. 56(6). 796–807. 69 indexed citations
10.
Chang, Kenneth, et al.. (2013). Synthesis and NMR Assignment of the Two Diastereomers of 8,6′-Cyclo-2′,6′-Dideoxyadenosine. Nucleosides Nucleotides & Nucleic Acids. 32(6). 320–332. 1 indexed citations
11.
Chang, Kenneth, et al.. (2013). Creating an miR30-Based shRNA Vector. Cold Spring Harbor Protocols. 2013(7). pdb.prot075853–pdb.prot075853. 32 indexed citations
12.
Dahlman, Kimberly B., Joel S. Parker, Tambudzai Shamu, et al.. (2012). Modulators of Prostate Cancer Cell Proliferation and Viability Identified by Short-Hairpin RNA Library Screening. PLoS ONE. 7(4). e34414–e34414. 23 indexed citations
13.
Bivona, Trever G., Haley Hieronymus, Joel S. Parker, et al.. (2011). FAS and NF-κB signalling modulate dependence of lung cancers on mutant EGFR. Nature. 471(7339). 523–526. 315 indexed citations
14.
Fellmann, Christof, Johannes Zuber, Katherine McJunkin, et al.. (2011). Functional Identification of Optimized RNAi Triggers Using a Massively Parallel Sensor Assay. Molecular Cell. 41(6). 733–746. 166 indexed citations
15.
Silva, José M., Krista Marran, Joel S. Parker, et al.. (2008). Profiling Essential Genes in Human Mammary Cells by Multiplex RNAi Screening. Science. 319(5863). 617–620. 225 indexed citations
16.
Schlabach, Michael R., Ji Luo, Nicole L. Solimini, et al.. (2008). Cancer Proliferation Gene Discovery Through Functional Genomics. Science. 319(5863). 620–624. 291 indexed citations
17.
Chang, Kenneth, Stephen J. Elledge, & Gregory J. Hannon. (2006). Lessons from Nature: microRNA-based shRNA libraries. Nature Methods. 3(9). 707–714. 209 indexed citations
18.
Silva, José, Kenneth Chang, Gregory J. Hannon, & Fabiola V. Rivas. (2004). RNA-interference-based functional genomics in mammalian cells: reverse genetics coming of age. Oncogene. 23(51). 8401–8409. 85 indexed citations
19.
Paddison, Patrick J., Michele A. Cleary, Kenneth Chang, et al.. (2004). Cloning of short hairpin RNAs for gene knockdown in mammalian cells. Nature Methods. 1(2). 163–167. 146 indexed citations
20.
Cleary, Michele A., K. Kilian, Yanqun Wang, et al.. (2004). Production of complex nucleic acid libraries using highly parallel in situ oligonucleotide synthesis. Nature Methods. 1(3). 241–248. 87 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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