Layne T. Watson

1.2k total citations
59 papers, 867 citations indexed

About

Layne T. Watson is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Layne T. Watson has authored 59 papers receiving a total of 867 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 9 papers in Genetics. Recurrent topics in Layne T. Watson's work include Gene Regulatory Network Analysis (21 papers), Bioinformatics and Genomic Networks (12 papers) and Microbial Metabolic Engineering and Bioproduction (11 papers). Layne T. Watson is often cited by papers focused on Gene Regulatory Network Analysis (21 papers), Bioinformatics and Genomic Networks (12 papers) and Microbial Metabolic Engineering and Bioproduction (11 papers). Layne T. Watson collaborates with scholars based in United States, Italy and India. Layne T. Watson's co-authors include C. Y. Wang, John J. Tyson, Naren Ramakrishnan, William H. Mason, Raphael T. Haftka, Bernard Grossman, Liqing Zhang, Chuck Baker, Anthony Giunta and Clifford A. Shaffer and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Chemical Physics and Bioinformatics.

In The Last Decade

Layne T. Watson

58 papers receiving 823 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Layne T. Watson United States	16	278	129	110	99	96	59	867
Chen Gao China	21	157 0.6×	57 0.4×	72 0.7×	28 0.3×	25 0.3×	98	1.7k
Marco Castellani United Kingdom	18	55 0.2×	115 0.9×	49 0.4×	101 1.0×	8 0.1×	58	870
Yubin Yubin China	9	120 0.4×	30 0.2×	68 0.6×	26 0.3×	32 0.3×	48	1.0k
Yuqian Guo China	23	967 3.5×	363 2.8×	146 1.3×	77 0.8×	20 0.2×	104	1.8k
Stefan Posch Germany	17	551 2.0×	30 0.2×	78 0.7×	87 0.9×	11 0.1×	105	1.1k
Stefan Streif Germany	18	282 1.0×	136 1.1×	38 0.3×	46 0.5×	150 1.6×	111	1.1k
Olivier Strauss France	17	26 0.1×	84 0.7×	32 0.3×	22 0.2×	20 0.2×	82	1.0k
Terence Soule United States	15	154 0.6×	113 0.9×	32 0.3×	34 0.3×	8 0.1×	70	918
Ritabrata Dutta United Kingdom	13	40 0.1×	14 0.1×	63 0.6×	93 0.9×	15 0.2×	31	511

Countries citing papers authored by Layne T. Watson

Since Specialization

Citations

This map shows the geographic impact of Layne T. Watson'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 Layne T. Watson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Layne T. Watson more than expected).

Fields of papers citing papers by Layne T. Watson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Layne T. Watson. 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 Layne T. Watson. The network helps show where Layne T. Watson may publish in the future.

Co-authorship network of co-authors of Layne T. Watson

This figure shows the co-authorship network connecting the top 25 collaborators of Layne T. Watson. A scholar is included among the top collaborators of Layne T. Watson 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 Layne T. Watson. Layne T. Watson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ji, Ming, et al.. (2023). DeepMicroGen: a generative adversarial network-based method for longitudinal microbiome data imputation. Bioinformatics. 39(5). 21 indexed citations

Watson, Layne T., et al.. (2022). Modeling the temporal dynamics of master regulators and CtrA proteolysis in Caulobacter crescentus cell cycle. PLoS Computational Biology. 18(1). e1009847–e1009847. 3 indexed citations

Zhang, Yonggang, Fang Li, Xiao Xiao, et al.. (2018). Comprehensive off-target analysis of dCas9-SAM-mediated HIV reactivation via long noncoding RNA and mRNA profiling. BMC Medical Genomics. 11(1). 78–78. 19 indexed citations

Wu, Xiaowei, et al.. (2017). UPS-indel: a Universal Positioning System for Indels. Scientific Reports. 7(1). 14106–14106. 6 indexed citations

Wang, S., et al.. (2017). Efficient implementation of the hybrid method for stochastic simulation of biochemical systems. 2(2). 1750006–1750006. 5 indexed citations

Oguz, Cihan, Layne T. Watson, William T. Baumann, & John J. Tyson. (2017). Predicting network modules of cell cycle regulators using relative protein abundance statistics. BMC Systems Biology. 11(1). 30–30. 9 indexed citations

Schetz, Joseph A., et al.. (2015). Development of a multidisciplinary design optimization framework for an efficient supersonic air vehicle. 2(1). 17–44. 6 indexed citations

Samuels, David C., et al.. (2015). Randomness in the hybrid modeling and simulation of insulin secretion pathways in pancreatic islets. Tsinghua Science & Technology. 20(5). 441–452. 1 indexed citations

Watson, Layne T., et al.. (2015). Predicting the combined effect of multiple genetic variants. Human Genomics. 9(1). 18–18. 12 indexed citations

10.

Schetz, Joseph A., et al.. (2015). Development of a multidisciplinary design optimization framework for an efficient supersonic air vehicle. 2(1). 17–44. 13 indexed citations

11.

Schetz, Joseph A., et al.. (2015). Reevaluating conceptual design fidelity: An efficient supersonic air vehicle design case. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 230(3). 581–598. 7 indexed citations

12.

Palmisano, Alida, et al.. (2015). JigCell Run Manager (JC-RM): a tool for managing large sets of biochemical model parametrizations. BMC Systems Biology. 9(1). 95–95. 3 indexed citations

13.

Oguz, Cihan, Alida Palmisano, Teeraphan Laomettachit, et al.. (2014). A Stochastic Model Correctly Predicts Changes in Budding Yeast Cell Cycle Dynamics upon Periodic Expression of CLN2. PLoS ONE. 9(5). e96726–e96726. 8 indexed citations

14.

Palmisano, Alida, et al.. (2014). Multistate Model Builder (MSMB): a flexible editor for compact biochemical models. BMC Systems Biology. 8(1). 42–42. 10 indexed citations

15.

Ahn, Tae-Hyuk, Pengyuan Wang, Layne T. Watson, et al.. (2009). Stochastic cell cycle modeling for budding yeast. Spring Simulation Multiconference. 113. 3 indexed citations

16.

Ramakrishnan, Naren, et al.. (2009). SIMULTANEOUSLY SEGMENTING MULTIPLE GENE EXPRESSION TIME COURSES BY ANALYZING CLUSTER DYNAMICS. Journal of Bioinformatics and Computational Biology. 7(2). 339–356. 6 indexed citations

17.

Ahn, Tae-Hyuk, Yang Cao, & Layne T. Watson. (2008). Stochastic Simulation Algorithms for Chemical Reactions.. 431–436. 4 indexed citations

18.

Shaffer, Clifford A., et al.. (2006). The JigCell Model Builder: A Spreadsheet Interface for Creating Biochemical Reaction Network Models. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 3(2). 155–164. 22 indexed citations

19.

Tyson, John J., et al.. (2005). Globally optimised parameters for a model of mitotic control in frog egg extracts. PubMed. 152(2). 81–81. 34 indexed citations

20.

Jiang, Shu, Layne T. Watson, Naren Ramakrishnan, Frederick A. Kamke, & Chris North. (2005). Unification of Problem Solving Environment Implementation Layers with XML. VTechWorks (Virginia Tech). 2 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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