Jay W. Warrick

1.4k total citations
37 papers, 1.1k citations indexed

About

Jay W. Warrick is a scholar working on Biomedical Engineering, Oncology and Molecular Biology. According to data from OpenAlex, Jay W. Warrick has authored 37 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Biomedical Engineering, 11 papers in Oncology and 8 papers in Molecular Biology. Recurrent topics in Jay W. Warrick's work include Microfluidic and Bio-sensing Technologies (14 papers), 3D Printing in Biomedical Research (13 papers) and Microfluidic and Capillary Electrophoresis Applications (12 papers). Jay W. Warrick is often cited by papers focused on Microfluidic and Bio-sensing Technologies (14 papers), 3D Printing in Biomedical Research (13 papers) and Microfluidic and Capillary Electrophoresis Applications (12 papers). Jay W. Warrick collaborates with scholars based in United States, South Korea and Puerto Rico. Jay W. Warrick's co-authors include David J. Beebe, Ivar Meyvantsson, Erwin Berthier, Steven Hayes, Jongil Ju, William L. Murphy, John Yin, Maribella Domenech, Andrea C. Timm and Gary E. Lyons and has published in prestigious journals such as Nature Communications, The Journal of Immunology and PLoS ONE.

In The Last Decade

Jay W. Warrick

37 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jay W. Warrick United States 19 729 287 181 126 80 37 1.1k
Hoon Suk Rho Netherlands 15 864 1.2× 312 1.1× 105 0.6× 96 0.8× 48 0.6× 31 1.3k
Gulnaz Stybayeva United States 21 822 1.1× 470 1.6× 156 0.9× 86 0.7× 56 0.7× 64 1.3k
Reza Riahi United States 17 681 0.9× 335 1.2× 167 0.9× 79 0.6× 189 2.4× 18 1.1k
David J. Guckenberger United States 14 757 1.0× 198 0.7× 108 0.6× 185 1.5× 53 0.7× 21 1.0k
Andrei Georgescu United States 10 700 1.0× 292 1.0× 257 1.4× 21 0.2× 73 0.9× 14 1.1k
Yaara Porat United States 13 435 0.6× 362 1.3× 173 1.0× 36 0.3× 86 1.1× 58 1.2k
Rami El Assal United States 12 749 1.0× 175 0.6× 189 1.0× 31 0.2× 93 1.2× 20 1.1k
Eric Freund Germany 26 361 0.5× 703 2.4× 169 0.9× 217 1.7× 105 1.3× 57 1.7k
Sam H. Au United Kingdom 13 893 1.2× 232 0.8× 450 2.5× 334 2.7× 114 1.4× 25 1.3k
Emily Jackson United States 9 433 0.6× 364 1.3× 88 0.5× 40 0.3× 46 0.6× 13 721

Countries citing papers authored by Jay W. Warrick

Since Specialization
Citations

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

Fields of papers citing papers by Jay W. Warrick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay W. Warrick. 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 Jay W. Warrick. The network helps show where Jay W. Warrick may publish in the future.

Co-authorship network of co-authors of Jay W. Warrick

This figure shows the co-authorship network connecting the top 25 collaborators of Jay W. Warrick. A scholar is included among the top collaborators of Jay W. Warrick 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 Jay W. Warrick. Jay W. Warrick 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.
Jong, Daniëlle de, Cody S. Carrell, Jay W. Warrick, et al.. (2024). Flow-S: A Field-Deployable Device with Minimal Hands-On Effort to Concentrate and Quantify Schistosoma Circulating Anodic Antigen (CAA) from Large Urine Volumes. Diagnostics. 14(8). 820–820. 1 indexed citations
2.
Callander, Natalie S., et al.. (2022). Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution. Integrative Biology. 14(3). 49–61. 1 indexed citations
3.
Warrick, Jay W., et al.. (2022). A Hyaluronan and Proteoglycan Link Protein 1 Matrikine: Role of Matrix Metalloproteinase 2 in Multiple Myeloma NF-κB Activation and Drug Resistance. Molecular Cancer Research. 20(9). 1456–1466. 11 indexed citations
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Regier, Mary C., Jay W. Warrick, Lil Pabon, et al.. (2019). User-defined morphogen patterning for directing human cell fate stratification. Scientific Reports. 9(1). 6433–6433. 11 indexed citations
7.
Seenivasan, R., et al.. (2018). Integrating electrochemical immunosensing and cell adhesion technologies for cancer cell detection and enumeration. Electrochimica Acta. 286. 205–211. 10 indexed citations
8.
Morgan, Molly, et al.. (2018). Mammary fibroblasts reduce apoptosis and speed estrogen-induced hyperplasia in an organotypic MCF7-derived duct model. Scientific Reports. 8(1). 7139–7139. 38 indexed citations
9.
Seenivasan, R., Chandra K. Singh, Jay W. Warrick, Nihal Ahmad, & Sundaram Gunasekaran. (2017). Microfluidic-integrated patterned ITO immunosensor for rapid detection of prostate-specific membrane antigen biomarker in prostate cancer. Biosensors and Bioelectronics. 95. 160–167. 33 indexed citations
10.
Warrick, Jay W., David J. Guckenberger, Jamie M. Sperger, et al.. (2017). Interrogating Bronchoalveolar Lavage Samples via Exclusion-Based Analyte Extraction. SLAS TECHNOLOGY. 22(3). 348–357. 1 indexed citations
11.
Schehr, Jennifer L., Jay W. Warrick, David J. Guckenberger, et al.. (2016). High Specificity in Circulating Tumor Cell Identification Is Required for Accurate Evaluation of Programmed Death-Ligand 1. PLoS ONE. 11(7). e0159397–e0159397. 55 indexed citations
12.
Ju, Jongil & Jay W. Warrick. (2013). Passive micromixer using by convection and surface tension effects with air-liquid interface. BioChip Journal. 7(4). 361–366. 14 indexed citations
13.
Casavant, Benjamin P., Rachel Mosher, Jay W. Warrick, et al.. (2013). A negative selection methodology using a microfluidic platform for the isolation and enumeration of circulating tumor cells. Methods. 64(2). 137–143. 44 indexed citations
14.
Warrick, Jay W., Edmond W. K. Young, Eric G. Schmuck, Kurt W. Saupe, & David J. Beebe. (2013). High-content adhesion assay to address limited cell samples. Integrative Biology. 5(4). 720–720. 11 indexed citations
15.
Domenech, Maribella, Hongmei Yu, Jay W. Warrick, et al.. (2009). Cellular observations enabled by microculture: paracrine signaling and population demographics. Integrative Biology. 1(3). 267–267. 69 indexed citations
16.
Zhu, Ying, et al.. (2009). Infection on a chip: a microscale platform for simple and sensitive cell-based virus assays. Biomedical Microdevices. 11(3). 565–570. 23 indexed citations
17.
Berthier, Erwin, et al.. (2008). Managing evaporation for more robust microscale assays : Part 1. Volume loss in high throughput assays. Lab on a Chip. 8(6). 852–852. 91 indexed citations
18.
Jongpaiboonkit, Leenaporn, W. J. King, Gary E. Lyons, et al.. (2008). An adaptable hydrogel array format for 3-dimensional cell culture and analysis. Biomaterials. 29(23). 3346–3356. 84 indexed citations
19.
Meyvantsson, Ivar, et al.. (2008). Automated cell culture in high density tubeless microfluidic device arrays. Lab on a Chip. 8(5). 717–717. 148 indexed citations
20.
Warrick, Jay W., Ivar Meyvantsson, Jongil Ju, & David J. Beebe. (2007). High-throughput microfluidics: improved sample treatment and washing over standard wells. Lab on a Chip. 7(3). 316–316. 46 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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