Cranos Williams

2.0k total citations
61 papers, 1.0k citations indexed

About

Cranos Williams is a scholar working on Molecular Biology, Plant Science and Biomedical Engineering. According to data from OpenAlex, Cranos Williams has authored 61 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 26 papers in Plant Science and 13 papers in Biomedical Engineering. Recurrent topics in Cranos Williams's work include Plant Gene Expression Analysis (12 papers), Plant Molecular Biology Research (11 papers) and Plant nutrient uptake and metabolism (10 papers). Cranos Williams is often cited by papers focused on Plant Gene Expression Analysis (12 papers), Plant Molecular Biology Research (11 papers) and Plant nutrient uptake and metabolism (10 papers). Cranos Williams collaborates with scholars based in United States, China and Denmark. Cranos Williams's co-authors include Jack Wang, Vincent L. Chiang, Ronald R. Sederoff, Joel J. Ducoste, Rosangela Sozzani, Quanzi Li, David C. Muddiman, Hsi-Chuan Chen, Natalie M. Clark and María Angels de Luis Balaguer and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Cranos Williams

57 papers receiving 982 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cranos Williams United States 18 564 420 223 77 76 61 1.0k
Sebastian Klie Germany 17 1.1k 2.0× 672 1.6× 117 0.5× 57 0.7× 46 0.6× 24 1.7k
Zhonghua Sheng China 18 372 0.7× 836 2.0× 65 0.3× 48 0.6× 91 1.2× 61 1.1k
Heiko Neuweger Germany 19 1.0k 1.8× 232 0.6× 361 1.6× 38 0.5× 114 1.5× 25 1.6k
Xiaoyu Li China 21 461 0.8× 984 2.3× 65 0.3× 30 0.4× 59 0.8× 82 1.4k
Jaspreet Singh India 13 333 0.6× 867 2.1× 199 0.9× 11 0.1× 96 1.3× 30 1.7k
K.M. Lewis United States 16 308 0.5× 618 1.5× 86 0.4× 29 0.4× 45 0.6× 25 1.0k
Martin Hoffmann Germany 8 512 0.9× 119 0.3× 84 0.4× 45 0.6× 19 0.3× 25 939
Yun Zhou China 28 1.1k 2.0× 1.7k 4.1× 100 0.4× 52 0.7× 59 0.8× 137 2.7k
Yating Zhang China 20 499 0.9× 627 1.5× 200 0.9× 23 0.3× 18 0.2× 79 1.3k
Joan Albiol Spain 25 1.3k 2.3× 168 0.4× 476 2.1× 127 1.6× 52 0.7× 48 1.7k

Countries citing papers authored by Cranos Williams

Since Specialization
Citations

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

Fields of papers citing papers by Cranos Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cranos Williams

This figure shows the co-authorship network connecting the top 25 collaborators of Cranos Williams. A scholar is included among the top collaborators of Cranos Williams 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 Cranos Williams. Cranos Williams 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.
Pecota, Kenneth V., et al.. (2025). Deployment and analysis of instance segmentation algorithm for in‐field yield estimation of sweet potatoes. SHILAP Revista de lepidopterología. 8(1).
2.
Williams, Cranos, et al.. (2025). High‐throughput classification and quantification of skinning phenotype in sweet potatoes. SHILAP Revista de lepidopterología. 8(1).
3.
Williams, Cranos, et al.. (2024). Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo. Biology Open. 13(9). 1 indexed citations
4.
Clark, Natalie M., et al.. (2022). POPEYE intercellular localization mediates cell-specific iron deficiency responses. PLANT PHYSIOLOGY. 190(3). 2017–2032. 10 indexed citations
5.
Akyol, Turgut Yigit, et al.. (2022). FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus. PLANT PHYSIOLOGY. 191(2). 1138–1152. 5 indexed citations
6.
Busato, Sebastiano, et al.. (2022). Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies. Current Opinion in Plant Biology. 71. 102326–102326. 15 indexed citations
7.
Matthews, Megan L., Jack Wang, Ronald R. Sederoff, Vincent L. Chiang, & Cranos Williams. (2020). Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa. PLoS Computational Biology. 16(4). e1007197–e1007197. 15 indexed citations
8.
Williams, Cranos, et al.. (2020). MAGIC: Live imaging of cellular division in plant seedlings using lightsheet microscopy. Methods in cell biology. 160. 405–418. 1 indexed citations
9.
Long, Terri A., et al.. (2020). BioVision Tracker: A semi-automated image analysis software for spatiotemporal gene expression tracking in Arabidopsis thaliana. Methods in cell biology. 160. 419–436. 1 indexed citations
10.
Long, Terri A., et al.. (2020). Computational solutions for modeling and controlling plant response to abiotic stresses: a review with focus on iron deficiency. Current Opinion in Plant Biology. 57. 8–15. 17 indexed citations
11.
Broeck, Lisa Van den, et al.. (2020). Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling. Frontiers in Genetics. 11. 457–457. 36 indexed citations
12.
Argueso, Cristiana T., Sarah M. Assmann, Kenneth D. Birnbaum, et al.. (2019). Directions for research and training in plant omics: Big Questions and Big Data. Plant Direct. 3(4). e00133–e00133. 21 indexed citations
13.
Wang, Jack, Megan L. Matthews, Cranos Williams, et al.. (2018). Flux modeling for monolignol biosynthesis. Current Opinion in Biotechnology. 56. 187–192. 45 indexed citations
14.
Balaguer, María Angels de Luis, Adam Fisher, Natalie M. Clark, et al.. (2017). Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells. Proceedings of the National Academy of Sciences. 114(36). E7632–E7640. 67 indexed citations
15.
Matthiadis, Anna, Siobhán M. Brady, Joel J. Ducoste, et al.. (2015). Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response. PLoS ONE. 10(8). e0136591–e0136591. 13 indexed citations
16.
17.
18.
Marvel, Skylar W. & Cranos Williams. (2012). Set membership experimental design for biological systems. BMC Systems Biology. 6(1). 21–21. 12 indexed citations
19.
Williams, Cranos, et al.. (2012). A novel cost function to estimate parameters of oscillatory biochemical systems. PubMed. 2012(1). 3–3. 2 indexed citations
20.
Rasure, John, et al.. (1991). A desing environment for multidimensional signal processing. 24(1). 18–33. 1 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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