Anne M. Andrews

13.6k total citations · 4 hit papers
142 papers, 7.7k citations indexed

About

Anne M. Andrews is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Biomedical Engineering. According to data from OpenAlex, Anne M. Andrews has authored 142 papers receiving a total of 7.7k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 43 papers in Cellular and Molecular Neuroscience and 33 papers in Biomedical Engineering. Recurrent topics in Anne M. Andrews's work include Neurotransmitter Receptor Influence on Behavior (30 papers), Neuroscience and Neuropharmacology Research (22 papers) and Molecular Junctions and Nanostructures (19 papers). Anne M. Andrews is often cited by papers focused on Neurotransmitter Receptor Influence on Behavior (30 papers), Neuroscience and Neuropharmacology Research (22 papers) and Molecular Junctions and Nanostructures (19 papers). Anne M. Andrews collaborates with scholars based in United States, Germany and Spain. Anne M. Andrews's co-authors include Paul S. Weiss, Dennis L. Murphy, Klaus‐Peter Lesch, Nako Nakatsuka, Christine H. Wichems, Chuanzhen Zhao, Kevin M. Cheung, Hongyan Yang, Kyung-Ae Yang and Milan N. Stojanović and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Anne M. Andrews

137 papers receiving 7.5k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Aptamer–field-effect transistors overcome Debye length limitations for small-molecule sensing

2018 717 citations Nako Nakatsuka, Kyung-Ae Yang et al. Science profile →
Altered Brain Serotonin Homeostasis and Locomotor Insensitivity to 3,4-Methylenedioxymethamphetamine (“Ecstasy”) in Serotonin Transporter-Deficient Mic

1998 588 citations Dennis L. Murphy, Anne M. Andrews et al. profile →
Interplay between materials and microfluidics

2017 337 citations Grissel Trujillo‐de Santiago, Mario Moisés Álvarez et al. profile →
Wearable aptamer-field-effect transistor sensing system for noninvasive cortisol monitoring

2022 276 citations Bo Wang, Chuanzhen Zhao et al. Science Advances profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Anne M. Andrews United States	47	2.7k	2.5k	2.0k	1.7k	665	142	7.7k
Polina Anikeeva United States	45	1.1k 0.4×	4.1k 1.6×	3.1k 1.6×	2.7k 1.6×	520 0.8×	96	9.5k
Owe Orwar Sweden	43	2.8k 1.0×	1.1k 0.4×	2.6k 1.3×	507 0.3×	581 0.9×	146	6.1k
Robert T. Kennedy United States	76	7.6k 2.8×	4.0k 1.6×	6.3k 3.2×	2.3k 1.3×	112 0.2×	351	19.2k
Joel Greenberg United States	54	2.2k 0.8×	1.8k 0.7×	5.1k 2.6×	1.2k 0.7×	251 0.4×	248	13.4k
Ralph N. Adams United States	50	1.6k 0.6×	2.7k 1.1×	800 0.4×	3.3k 1.9×	186 0.3×	144	9.6k
Mogens Brøndsted Nielsen Denmark	57	3.8k 1.4×	5.3k 2.1×	426 0.2×	2.1k 1.2×	596 0.9×	442	13.7k
Fabio Benfenati Italy	77	10.7k 4.0×	11.6k 4.6×	1.6k 0.8×	833 0.5×	225 0.3×	437	22.0k
Feng Zhang China	52	4.9k 1.8×	8.5k 3.4×	4.8k 2.4×	1.6k 0.9×	404 0.6×	341	19.6k
Maria Erecińska United States	61	6.9k 2.6×	4.2k 1.7×	679 0.3×	420 0.2×	412 0.6×	222	13.3k
Andrew G. Ewing United States	74	5.8k 2.1×	3.7k 1.5×	6.2k 3.1×	4.4k 2.5×	595 0.9×	398	17.6k

Countries citing papers authored by Anne M. Andrews

Since Specialization

Citations

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

Fields of papers citing papers by Anne M. Andrews

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anne M. Andrews. 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 Anne M. Andrews. The network helps show where Anne M. Andrews may publish in the future.

Co-authorship network of co-authors of Anne M. Andrews

This figure shows the co-authorship network connecting the top 25 collaborators of Anne M. Andrews. A scholar is included among the top collaborators of Anne M. Andrews 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 Anne M. Andrews. Anne M. Andrews 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

Tyler, Matthew, et al.. (2025). GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability. Mathematical Biosciences. 387. 109485–109485.

Yang, Kyung-Ae, Saswata Banerjee, Steven K. Taylor, et al.. (2023). A functional group–guided approach to aptamers for small molecules. Science. 380(6648). 942–948. 94 indexed citations

Wang, Bo, Chuanzhen Zhao, Zhaoqing Wang, et al.. (2022). Wearable aptamer-field-effect transistor sensing system for noninvasive cortisol monitoring. Science Advances. 8(1). eabk0967–eabk0967. 276 indexed citations breakdown →

Khademhosseini, Ali, André E. Nel, Christopher J. DeSantis, et al.. (2019). Nanoscience and Nanotechnology at UCLA. ACS Nano. 13(6). 6127–6129. 1 indexed citations

Nakatsuka, Nako, Kyung-Ae Yang, John M. Abendroth, et al.. (2018). Aptamer–field-effect transistors overcome Debye length limitations for small-molecule sensing. Science. 362(6412). 319–324. 717 indexed citations breakdown →

Nakatsuka, Nako, Mohammad Mahdi Hasani‐Sadrabadi, Kevin M. Cheung, et al.. (2018). Polyserotonin Nanoparticles as Multifunctional Materials for Biomedical Applications. ACS Nano. 12(5). 4761–4774. 63 indexed citations

Álvarez, Mario Moisés, Joanna Aizenberg, Mostafa Analoui, et al.. (2017). Emerging Trends in Micro- and Nanoscale Technologies in Medicine: From Basic Discoveries to Translation. ACS Nano. 11(6). 5195–5214. 101 indexed citations

Slaughter, Liane S., Kevin M. Cheung, Sami Kaappa, et al.. (2017). Patterning of supported gold monolayers via chemical lift-off lithography. Beilstein Journal of Nanotechnology. 8. 2648–2661. 16 indexed citations

Andrews, Anne M., Wei‐Ssu Liao, & Paul S. Weiss. (2016). Double-Sided Opportunities Using Chemical Lift-Off Lithography. Accounts of Chemical Research. 49(8). 1449–1457. 44 indexed citations

10.

Cao, Huan H., Nako Nakatsuka, Andrew C. Serino, et al.. (2015). Controlled DNA Patterning by Chemical Lift-Off Lithography: Matrix Matters. ACS Nano. 9(11). 11439–11454. 38 indexed citations

11.

Claridge, Shelley A., Wei‐Ssu Liao, John C. Thomas, et al.. (2012). From the bottom up: dimensional control and characterization in molecular monolayers. Chemical Society Reviews. 42(7). 2725–2745. 146 indexed citations

12.

Vaish, Amit, et al.. (2011). Tuning Stamp Surface Energy for Soft Lithography of Polar Molecules to Fabricate Bioactive Small‐Molecule Microarrays. Small. 7(10). 1471–1479. 24 indexed citations

13.

Blizard, David A., et al.. (2010). Low-stress Route Learning Using the Lashley III Maze in Mice. Journal of Visualized Experiments. 4 indexed citations

14.

Murphy, Dennis L., Meredith A. Fox, Kiara R. Timpano, et al.. (2008). How the serotonin story is being rewritten by new gene-based discoveries principally related to SLC6A4, the serotonin transporter gene, which functions to influence all cellular serotonin systems. Neuropharmacology. 55(6). 932–960. 169 indexed citations

15.

Fox, Meredith A., Anne M. Andrews, Jens R. Wendland, et al.. (2007). A pharmacological analysis of mice with a targeted disruption of the serotonin transporter. Psychopharmacology. 195(2). 147–166. 48 indexed citations

16.

Guiard, Bruno P., Denis J. David, Thierry Deltheil, et al.. (2007). Brain-derived neurotrophic factor-deficient mice exhibit a hippocampal hyperserotonergic phenotype. The International Journal of Neuropsychopharmacology. 11(1). 79–92. 55 indexed citations

17.

Unger, Erica L., David J. Eve, Xiomara A. Perez, et al.. (2005). Locomotor hyperactivity and alterations in dopamine neurotransmission are associated with overexpression of A53T mutant human α-synuclein in mice. Neurobiology of Disease. 21(2). 431–443. 100 indexed citations

18.

Perez, Xiomara A. & Anne M. Andrews. (2004). Chronoamperometry To Determine Differential Reductions in Uptake in Brain Synaptosomes from Serotonin Transporter Knockout Mice. Analytical Chemistry. 77(3). 818–826. 34 indexed citations

19.

Unger, Erica L., Pascale Mazzola‐Pomietto, Dennis L. Murphy, & Anne M. Andrews. (2002). 2′-NH2-MPTP [1-Methyl-4-(2′-aminophenyl)-1,2,3,6-tetrahydropyridine] Depletes Serotonin and Norepinephrine in Rats: A Comparison with 2′-CH3-MPTP [1-Methyl-4-(2′-methylphenyl)-1,2,3,6-tetrahydropyridine]. Journal of Pharmacology and Experimental Therapeutics. 303(2). 527–533. 9 indexed citations

20.

Tella, Srihari R., Bruce Ladenheim, Anne M. Andrews, Steven R. Goldberg, & Jean Lud Cadet. (1996). Differential Reinforcing Effects of Cocaine and GBR-12909: Biochemical Evidence for Divergent Neuroadaptive Changes in the Mesolimbic Dopaminergic System. Journal of Neuroscience. 16(23). 7416–7427. 72 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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