Stephen C.J. Parker

16.3k total citations
49 papers, 1.9k citations indexed

About

Stephen C.J. Parker is a scholar working on Molecular Biology, Genetics and Surgery. According to data from OpenAlex, Stephen C.J. Parker has authored 49 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 14 papers in Genetics and 7 papers in Surgery. Recurrent topics in Stephen C.J. Parker's work include Genomics and Chromatin Dynamics (20 papers), Genomics and Phylogenetic Studies (8 papers) and RNA and protein synthesis mechanisms (8 papers). Stephen C.J. Parker is often cited by papers focused on Genomics and Chromatin Dynamics (20 papers), Genomics and Phylogenetic Studies (8 papers) and RNA and protein synthesis mechanisms (8 papers). Stephen C.J. Parker collaborates with scholars based in United States, China and United Kingdom. Stephen C.J. Parker's co-authors include Elliott H. Margulies, Thomas D. Tullius, Hatice Özel Abaan, Francis S. Collins, Michael R. Erdos, Loren Hansen, Subramanian S. Ajay, Daniel Quang, Arushi Varshney and Evan S. Snitkin and has published in prestigious journals such as Nature, Science and Nucleic Acids Research.

In The Last Decade

Stephen C.J. Parker

45 papers receiving 1.9k citations

Peers

Stephen C.J. Parker
Christina A. Harrington United States
Patrick Tarpey United Kingdom
Anne‐Françoise Roux France
Vasily Ramensky Russia
Tushar Bhangale United States
Marc A. Schaub United States
Eurie L. Hong United States
Daniel M. Jordan United States
Peter Krawitz Germany
Maya Kasowski United States
Christina A. Harrington United States
Stephen C.J. Parker
Citations per year, relative to Stephen C.J. Parker Stephen C.J. Parker (= 1×) peers Christina A. Harrington

Countries citing papers authored by Stephen C.J. Parker

Since Specialization
Citations

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

Fields of papers citing papers by Stephen C.J. Parker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen C.J. Parker. 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 Stephen C.J. Parker. The network helps show where Stephen C.J. Parker may publish in the future.

Co-authorship network of co-authors of Stephen C.J. Parker

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen C.J. Parker. A scholar is included among the top collaborators of Stephen C.J. Parker 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 Stephen C.J. Parker. Stephen C.J. Parker 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.
Perrin, Hannah J., Swarooparani Vadlamudi, Amy S. Etheridge, et al.. (2025). Genetic effects on chromatin accessibility uncover mechanisms of liver gene regulation and quantitative traits. Genome Research. 35(7). 1485–1502.
2.
3.
Feng, Fan, Jean‐Philippe Cartailler, Marcela Briššová, et al.. (2024). CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. PLoS Computational Biology. 20(8). e1012344–e1012344. 1 indexed citations
4.
Behera, Sairam, Jonathon LeFaive, Peter Orchard, et al.. (2023). FixItFelix: improving genomic analysis by fixing reference errors. Genome biology. 24(1). 31–31. 13 indexed citations
5.
Varshney, Arushi, Yasuhiro Kyono, Michael R. Erdos, et al.. (2021). A Transcription Start Site Map in Human Pancreatic Islets Reveals Functional Regulatory Signatures. Diabetes. 70(7). 1581–1591. 4 indexed citations
6.
Orchard, Peter, Nandini Manickam, Swarooparani Vadlamudi, et al.. (2021). Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Research. 31(12). 2258–2275. 35 indexed citations
7.
Albanus, Ricardo D’Oliveira, Yasuhiro Kyono, John Hensley, et al.. (2021). Chromatin information content landscapes inform transcription factor and DNA interactions. Nature Communications. 12(1). 1307–1307. 15 indexed citations
8.
Shrestha, Shristi, Diane C. Saunders, John T. Walker, et al.. (2021). Combinatorial transcription factor profiles predict mature and functional human islet α and β cells. JCI Insight. 6(18). 27 indexed citations
9.
Guan, Yuanfang, Hongjiu Zhang, Daniel Quang, et al.. (2019). Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers. Arthritis & Rheumatology. 71(12). 1987–1996. 94 indexed citations
10.
Kyono, Yasuhiro, Jacob O. Kitzman, & Stephen C.J. Parker. (2019). Genomic annotation of disease-associated variants reveals shared functional contexts. Diabetologia. 62(5). 735–743. 3 indexed citations
11.
Quang, Daniel, Yuanfang Guan, & Stephen C.J. Parker. (2018). YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. Bioinformatics. 34(20). 3578–3580. 9 indexed citations
12.
Zou, Luli S., Michael R. Erdos, D. Leland Taylor, et al.. (2018). BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues. BMC Genomics. 19(1). 390–390. 35 indexed citations
13.
Hosoya, Tomonori, Ricardo D’Oliveira Albanus, John Hensley, et al.. (2018). Global dynamics of stage-specific transcription factor binding during thymocyte development. Scientific Reports. 8(1). 5605–5605. 15 indexed citations
14.
Vahedi, Golnaz, Yuka Kanno, Yasuko Furumoto, et al.. (2015). Super-enhancers delineate disease-associated regulatory nodes in T cells. Nature. 520(7548). 558–562. 271 indexed citations
15.
Yang, Lin, Tianyin Zhou, Bradley J. Main, et al.. (2014). GBshape: a genome browser database for DNA shape annotations. Nucleic Acids Research. 43(D1). D103–D109. 44 indexed citations
16.
Parker, Stephen C.J., Jared J. Gartner, Xiaomu Wei, et al.. (2012). Mutational Signatures of De-Differentiation in Functional Non-Coding Regions of Melanoma Genomes. PLoS Genetics. 8(8). e1002871–e1002871. 8 indexed citations
17.
Ajay, Subramanian S., et al.. (2011). Accurate and comprehensive sequencing of personal genomes. Genome Research. 21(9). 1498–1505. 143 indexed citations
18.
Stitzel, Michael L., Praveen Sethupathy, Daniel S. Pearson, et al.. (2010). Global Epigenomic Analysis of Primary Human Pancreatic Islets Provides Insights into Type 2 Diabetes Susceptibility Loci. Cell Metabolism. 12(5). 443–455. 136 indexed citations
19.
Parker, Stephen C.J., Loren Hansen, Hatice Özel Abaan, Thomas D. Tullius, & Elliott H. Margulies. (2009). Local DNA Topography Correlates with Functional Noncoding Regions of the Human Genome. Science. 324(5925). 389–392. 156 indexed citations
20.
Gustafson, Adam M, Evan S. Snitkin, Stephen C.J. Parker, Charles DeLisi, & Simon Kasif. (2006). Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genomics. 7(1). 265–265. 114 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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