Malcolm Casale

2.2k total citations
19 papers, 1.1k citations indexed

About

Malcolm Casale is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Computational Mechanics. According to data from OpenAlex, Malcolm Casale has authored 19 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cellular and Molecular Neuroscience, 8 papers in Molecular Biology and 4 papers in Computational Mechanics. Recurrent topics in Malcolm Casale's work include Genetic Neurodegenerative Diseases (6 papers), Mitochondrial Function and Pathology (5 papers) and Advanced Numerical Analysis Techniques (4 papers). Malcolm Casale is often cited by papers focused on Genetic Neurodegenerative Diseases (6 papers), Mitochondrial Function and Pathology (5 papers) and Advanced Numerical Analysis Techniques (4 papers). Malcolm Casale collaborates with scholars based in United States, Italy and Czechia. Malcolm Casale's co-authors include Gary Lynch, Christine M. Gall, Christopher Rex, Lulu Y. Chen, Danielle A. Simmons, Leslie M. Thompson, Laura L Colgin, Fernando Brücher, J.E. Bobrow and Ryan G. Lim and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Malcolm Casale

18 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Malcolm Casale United States 13 537 436 224 156 142 19 1.1k
Yiheng Xie United States 10 322 0.6× 310 0.7× 145 0.6× 134 0.9× 62 0.4× 11 1.2k
Frank Angenstein Germany 26 616 1.1× 543 1.2× 157 0.7× 426 2.7× 323 2.3× 65 1.7k
Carlos Avendaño Spain 25 671 1.2× 195 0.4× 222 1.0× 411 2.6× 201 1.4× 65 1.4k
Krystel R. Huxlin United States 29 348 0.6× 543 1.2× 231 1.0× 896 5.7× 48 0.3× 132 2.3k
Carlos Avendaño Spain 18 227 0.4× 176 0.4× 139 0.6× 444 2.8× 62 0.4× 37 1.2k
Zhen Xu China 18 268 0.5× 404 0.9× 595 2.7× 78 0.5× 76 0.5× 49 1.4k
Bo Shui United States 16 316 0.6× 697 1.6× 164 0.7× 47 0.3× 49 0.3× 35 1.3k
Eunchai Kang United States 18 492 0.9× 725 1.7× 129 0.6× 125 0.8× 34 0.2× 28 1.6k
C.‐H. Berthold Sweden 16 466 0.9× 227 0.5× 80 0.4× 101 0.6× 69 0.5× 31 784
Yasushi Kishimoto Japan 24 791 1.5× 500 1.1× 388 1.7× 382 2.4× 89 0.6× 58 1.5k

Countries citing papers authored by Malcolm Casale

Since Specialization
Citations

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

Fields of papers citing papers by Malcolm Casale

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Malcolm Casale

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

All Works

19 of 19 papers shown
1.
Hernandez, Sarah, Ryan G. Lim, Mark Dane, et al.. (2022). An altered extracellular matrix–integrin interface contributes to Huntington’s disease-associated CNS dysfunction in glial and vascular cells. Human Molecular Genetics. 32(9). 1483–1496. 11 indexed citations
2.
Ochaba, Joseph, Gianna Fote, Alice Lau, et al.. (2019). IKKβ slows Huntington’s disease progression in R6/1 mice. Proceedings of the National Academy of Sciences. 116(22). 10952–10961. 25 indexed citations
3.
Kedaigle, Amanda J., Jack C. Reidling, Ryan G. Lim, et al.. (2019). Treatment with JQ1, a BET bromodomain inhibitor, is selectively detrimental to R6/2 Huntington’s disease mice. Human Molecular Genetics. 29(2). 202–215. 12 indexed citations
4.
Lim, Ryan G., Andrea M. Reyes-Ortiz, Sarah E. Lutz, et al.. (2017). Huntington’s Disease iPSC-Derived Brain Microvascular Endothelial Cells Reveal WNT-Mediated Angiogenic and Blood-Brain Barrier Deficits. Cell Reports. 19(7). 1365–1377. 191 indexed citations
5.
Salazar, Lisa, Pavel Krejčı́, April N. Meyer, et al.. (2014). Fibroblast Growth Factor Receptor 3 Interacts with and Activates TGFβ-Activated Kinase 1 Tyrosine Phosphorylation and NFκB Signaling in Multiple Myeloma and Bladder Cancer. PLoS ONE. 9(1). e86470–e86470. 27 indexed citations
6.
Rex, Christopher S., Laura L Colgin, Yousheng Jia, et al.. (2009). Origins of an Intrinsic Hippocampal EEG Pattern. PLoS ONE. 4(11). e7761–e7761. 15 indexed citations
7.
Casale, Malcolm, et al.. (2008). Auditory evoked potential abnormalities in cluster headache. Neuroreport. 19(16). 1633–1636. 12 indexed citations
8.
Apostol, Barbara L., Danielle A. Simmons, Chiara Zuccato, et al.. (2008). CEP-1347 reduces mutant huntingtin-associated neurotoxicity and restores BDNF levels in R6/2 mice. Molecular and Cellular Neuroscience. 39(1). 8–20. 67 indexed citations
9.
Chen, Lulu Y., Christopher Rex, Malcolm Casale, Christine M. Gall, & Gary Lynch. (2007). Changes in Synaptic Morphology Accompany Actin Signaling during LTP. Journal of Neuroscience. 27(20). 5363–5372. 232 indexed citations
10.
Simmons, Danielle A., et al.. (2007). Ferritin accumulation in dystrophic microglia is an early event in the development of Huntington's disease. Glia. 55(10). 1074–1084. 218 indexed citations
11.
Colgin, Laura L, et al.. (2003). Endogenous Waves in Hippocampal Slices. Journal of Neurophysiology. 89(1). 81–89. 84 indexed citations
12.
Jin, Yi, et al.. (2002). Identification of Diagnostic Evoked Response Potential Segments in Alzheimer's Disease. Experimental Neurology. 176(2). 269–276. 15 indexed citations
13.
Lathrop, R.H., Malcolm Casale, Douglas J. Tobias, Joan Marsh, & Leslie M. Thompson. (1998). Modeling protein homopolymeric repeats: possible polyglutamine structural motifs for Huntington's disease.. PubMed. 6. 105–14. 20 indexed citations
14.
Casale, Malcolm, J.E. Bobrow, & Robin Underwood. (1992). Trimmed-patch boundary elements: bridging the gap between solid modeling and engineering analysis. Computer-Aided Design. 24(4). 193–199. 4 indexed citations
15.
Casale, Malcolm & J.E. Bobrow. (1989). The analysis of solids without mesh generation using trimmed patch boundary elements. Engineering With Computers. 5(3-4). 249–257. 8 indexed citations
16.
Casale, Malcolm & J.E. Bobrow. (1989). A set operation algorithm for sculptured solids modeled with trimmed patches. Computer Aided Geometric Design. 6(3). 235–247. 21 indexed citations
17.
Casale, Malcolm. (1987). Free-Form Solid Modeling with Trimmed Surface Patches. IEEE Computer Graphics and Applications. 7(1). 33–43. 46 indexed citations
18.
Casale, Malcolm, et al.. (1985). An Overview of Analytic Solid Modeling. IEEE Computer Graphics and Applications. 5(2). 45–56. 46 indexed citations
19.
Casale, Malcolm, et al.. (1985). AnOverview ofAnalytic Solid Modeling. 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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