Mark Frasier

7.8k total citations
28 papers, 1.5k citations indexed

About

Mark Frasier is a scholar working on Neurology, Molecular Biology and Physiology. According to data from OpenAlex, Mark Frasier has authored 28 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Neurology, 6 papers in Molecular Biology and 6 papers in Physiology. Recurrent topics in Mark Frasier's work include Parkinson's Disease Mechanisms and Treatments (23 papers), Neurological disorders and treatments (13 papers) and Alzheimer's disease research and treatments (5 papers). Mark Frasier is often cited by papers focused on Parkinson's Disease Mechanisms and Treatments (23 papers), Neurological disorders and treatments (13 papers) and Alzheimer's disease research and treatments (5 papers). Mark Frasier collaborates with scholars based in United States, Germany and Canada. Mark Frasier's co-authors include Benjamin Wolozin, Kuldip D. Dave, Peter S. Choi, Heather M. Snyder, Catherine Theisler, Wassilios G. Meissner, David M. Weiner, Christopher G. Goetz, François Tison and Erwan Bézard and has published in prestigious journals such as Nature, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Mark Frasier

27 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Frasier United States 15 1.0k 449 448 379 264 28 1.5k
Nobutaka Sakae Japan 23 807 0.8× 342 0.8× 547 1.2× 517 1.4× 329 1.2× 53 1.8k
Sonia George United States 17 759 0.7× 400 0.9× 334 0.7× 345 0.9× 329 1.2× 23 1.3k
Heather McCann Australia 23 1.1k 1.0× 520 1.2× 811 1.8× 438 1.2× 410 1.6× 39 2.0k
Giulia Di Lazzaro Italy 21 933 0.9× 277 0.6× 355 0.8× 294 0.8× 223 0.8× 61 1.5k
Sudhakar Subramaniam United States 11 798 0.8× 477 1.1× 377 0.8× 570 1.5× 422 1.6× 19 1.7k
Ippolita Cantuti‐Castelvetri United States 21 729 0.7× 588 1.3× 408 0.9× 661 1.7× 234 0.9× 29 1.7k
Ayşe Ulusoy Germany 22 1.4k 1.3× 808 1.8× 415 0.9× 625 1.6× 351 1.3× 36 2.0k
Asako Yoritaka Japan 20 1.2k 1.1× 584 1.3× 436 1.0× 690 1.8× 311 1.2× 48 2.2k
Robert E. Drolet United States 15 1.0k 1.0× 639 1.4× 503 1.1× 502 1.3× 336 1.3× 18 1.7k
Hugo Vicente Miranda Portugal 19 657 0.6× 384 0.9× 594 1.3× 465 1.2× 261 1.0× 29 1.5k

Countries citing papers authored by Mark Frasier

Since Specialization
Citations

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

Fields of papers citing papers by Mark Frasier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Frasier

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Frasier. A scholar is included among the top collaborators of Mark Frasier 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 Mark Frasier. Mark Frasier 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.
Marebwa, Barbara, Tanya Simuni, Andrew Siderowf, et al.. (2022). Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors. Sensors. 22(18). 6831–6831. 13 indexed citations
2.
Frasier, Mark, Brian Fiske, & Todd Sherer. (2022). Precision medicine for Parkinson’s disease: The subtyping challenge. Frontiers in Aging Neuroscience. 14. 1064057–1064057. 6 indexed citations
3.
Li, Juan, Tiago Mestre, Brit Mollenhauer, et al.. (2022). Evaluation of the PREDIGT score’s performance in identifying newly diagnosed Parkinson’s patients without motor examination. npj Parkinson s Disease. 8(1). 94–94. 2 indexed citations
4.
Severson, Kristen, Lana M. Chahine, Mark Frasier, et al.. (2021). Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning. The Lancet Digital Health. 3(9). e555–e564. 52 indexed citations
5.
Chahine, Lana M., Andrew Siderowf, Janel Barnes, et al.. (2019). Predicting Progression in Parkinson’s Disease Using Baseline and 1-Year Change Measures. Journal of Parkinson s Disease. 9(4). 665–679. 14 indexed citations
6.
Goldman, Jennifer G., Howard Andrews, Amy W. Amara, et al.. (2017). Cerebrospinal fluid, plasma, and saliva in the BioFIND study: Relationships among biomarkers and Parkinson's disease Features. Movement Disorders. 33(2). 282–288. 117 indexed citations
7.
Mollenhauer, Brit, Richard Batrla, Omar M. A. El‐Agnaf, et al.. (2017). A user's guide for α‐synuclein biomarker studies in biological fluids: Perianalytical considerations. Movement Disorders. 32(8). 1117–1130. 51 indexed citations
8.
Eidson, Lori N., George T. Kannarkat, Christopher J. Barnum, et al.. (2017). Candidate inflammatory biomarkers display unique relationships with alpha-synuclein and correlate with measures of disease severity in subjects with Parkinson’s disease. Journal of Neuroinflammation. 14(1). 164–164. 69 indexed citations
9.
Frasier, Mark. (2016). Perspective: Data sharing for discovery. Nature. 538(7626). S4–S4. 5 indexed citations
10.
Eberling, Jamie L., Kuldip D. Dave, & Mark Frasier. (2013). α-synuclein Imaging: A Critical Need for Parkinson's Disease Research. Journal of Parkinson s Disease. 3(4). 565–567. 67 indexed citations
11.
Baptista, Marco A. S., Kuldip D. Dave, Mark Frasier, et al.. (2013). Loss of Leucine-Rich Repeat Kinase 2 (LRRK2) in Rats Leads to Progressive Abnormal Phenotypes in Peripheral Organs. PLoS ONE. 8(11). e80705–e80705. 137 indexed citations
12.
Frasier, Mark & Un Jung Kang. (2013). Parkinson's Disease Biomarkers: Resources for Discovery and Validation. Neuropsychopharmacology. 39(1). 241–242. 5 indexed citations
13.
Meissner, Wassilios G., Mark Frasier, Thomas Gasser, et al.. (2011). Priorities in Parkinson's disease research. Nature Reviews Drug Discovery. 10(5). 377–393. 342 indexed citations
14.
Frasier, Mark, Sohini Chowdhury, Jamie L. Eberling, & Todd Sherer. (2010). Biomarkers in Parkinson‘s Disease: A Funder‘s Perspective. Biomarkers in Medicine. 4(5). 723–729. 13 indexed citations
15.
Dorsey, E. Ray, Mark Frasier, Todd Sherer, et al.. (2009). Funding of Parkinson research from industry and US federal and foundation sources. Movement Disorders. 24(5). 731–737. 4 indexed citations
16.
17.
Landry, Michelle, Mark Frasier, Zhuo Chen, et al.. (2005). Fluoxetine treatment of prepubescent rats produces a selective functional reduction in the 5-HT2A receptor-mediated stimulation of oxytocin. Synapse. 58(2). 102–109. 16 indexed citations
18.
Frasier, Mark. (2004). Following the leader: fibrillization of ?-synuclein and tau. Experimental Neurology. 187(2). 235–239. 11 indexed citations
19.
Frasier, Mark, Mark Walzer, Lois E. McCarthy, et al.. (2004). Tau phosphorylation increases in symptomatic mice overexpressing A30P α-synuclein. Experimental Neurology. 192(2). 274–287. 91 indexed citations
20.
Snyder, Heather M., et al.. (2002). Magnesium Inhibits Spontaneous and Iron-induced Aggregation of α-Synuclein. Journal of Biological Chemistry. 277(18). 16116–16123. 180 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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