Thomas M. Morse

2.5k total citations
27 papers, 1.7k citations indexed

About

Thomas M. Morse is a scholar working on Molecular Biology, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Thomas M. Morse has authored 27 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in Cognitive Neuroscience and 7 papers in Artificial Intelligence. Recurrent topics in Thomas M. Morse's work include Neural dynamics and brain function (6 papers), Biomedical Text Mining and Ontologies (6 papers) and Bioinformatics and Genomic Networks (5 papers). Thomas M. Morse is often cited by papers focused on Neural dynamics and brain function (6 papers), Biomedical Text Mining and Ontologies (6 papers) and Bioinformatics and Genomic Networks (5 papers). Thomas M. Morse collaborates with scholars based in United States, Italy and Canada. Thomas M. Morse's co-authors include Shawn R. Lockery, Jonathan T. Pierce, Michael L. Hines, Nicholas T. Carnevale, Michele Migliore, Chiayu Q. Chiu, György Lür, Graham C. R. Ellis‐Davies, Michael J. Higley and Gordon M. Shepherd and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Thomas M. Morse

25 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas M. Morse United States 14 693 629 472 324 256 27 1.7k
Padraig Gleeson United Kingdom 17 608 0.9× 792 1.3× 527 1.1× 112 0.3× 82 0.3× 45 1.7k
Jessica C. Nelson United States 21 730 1.1× 598 1.0× 519 1.1× 193 0.6× 111 0.4× 29 1.5k
Hiroshi Kori Japan 23 474 0.7× 607 1.0× 731 1.5× 53 0.2× 328 1.3× 71 2.3k
Netta Cohen United Kingdom 19 550 0.8× 234 0.4× 405 0.9× 456 1.4× 276 1.1× 48 1.6k
Edward Soucy United States 13 777 1.1× 473 0.8× 296 0.6× 139 0.4× 139 0.5× 19 1.5k
Manuel Zimmer Austria 24 1.4k 2.1× 450 0.7× 854 1.8× 1.2k 3.8× 798 3.1× 44 3.5k
Saul Kato United States 7 400 0.6× 245 0.4× 132 0.3× 444 1.4× 344 1.3× 14 1.2k
Eviatar Yemini United States 14 299 0.4× 208 0.3× 271 0.6× 714 2.2× 394 1.5× 22 1.2k
Michinori Ichikawa Japan 18 895 1.3× 416 0.7× 825 1.7× 59 0.2× 88 0.3× 40 1.9k
Timothy O’Leary United Kingdom 20 1.0k 1.5× 918 1.5× 514 1.1× 67 0.2× 102 0.4× 44 1.7k

Countries citing papers authored by Thomas M. Morse

Since Specialization
Citations

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

Fields of papers citing papers by Thomas M. Morse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas M. Morse

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas M. Morse. A scholar is included among the top collaborators of Thomas M. Morse 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 Thomas M. Morse. Thomas M. Morse 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.
Cohen, B. A., S. J. Barber, F. A. J. Abernethy, et al.. (2025). The Peregrine Ion Trap Mass Spectrometer (PITMS): Results from a CLPS-delivered Mass Spectrometer. The Planetary Science Journal. 6(1). 14–14. 1 indexed citations
2.
Yuan, Peng, Лей Тонг, Thomas M. Morse, et al.. (2022). PLD3 affects axonal spheroids and network defects in Alzheimer’s disease. Nature. 612(7939). 328–337. 76 indexed citations
3.
McDougal, Robert A., et al.. (2018). Automated Metadata Suggestion During Repository Submission. Neuroinformatics. 17(3). 361–371. 5 indexed citations
4.
McDougal, Robert A., Thomas M. Morse, Ted Carnevale, et al.. (2016). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. Journal of Computational Neuroscience. 42(1). 1–10. 126 indexed citations
5.
Short, Shaina M., et al.. (2016). Respiration Gates Sensory Input Responses in the Mitral Cell Layer of the Olfactory Bulb. PLoS ONE. 11(12). e0168356–e0168356. 9 indexed citations
6.
McDougal, Robert A., Thomas M. Morse, Michael L. Hines, & Gordon M. Shepherd. (2015). ModelView for ModelDB: Online Presentation of Model Structure. Neuroinformatics. 13(4). 459–470. 12 indexed citations
7.
Raiola, Luca, et al.. (2014). Structural and Functional Characterization of a Complex between the Acidic Transactivation Domain of EBNA2 and the Tfb1/p62 Subunit of TFIIH. PLoS Pathogens. 10(3). e1004042–e1004042. 30 indexed citations
8.
Mas, Caroline, Thomas M. Morse, Paola Di Lello, et al.. (2011). Structural and functional characterization of an atypical activation domain in erythroid Krüppel-like factor (EKLF). Proceedings of the National Academy of Sciences. 108(26). 10484–10489. 43 indexed citations
9.
Morse, Thomas M.. (2010). Abnormal excitability of oblique dendrites implicated in early Alzheimer's: a computational study. Frontiers in Neural Circuits. 4. 31 indexed citations
10.
Gleeson, Padraig, Sharon Crook, Robert C. Cannon, et al.. (2010). NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Computational Biology. 6(6). e1000815–e1000815. 206 indexed citations
11.
Cheung, Kei-Hoi, Ee Jean Lim, Matthias Samwald, et al.. (2009). Approaches to neuroscience data integration. Briefings in Bioinformatics. 10(4). 345–353. 12 indexed citations
12.
Samwald, Matthias, Ernest Lim, Luis Marenco, et al.. (2009). Entrez Neuron RDFa: a pragmatic semantic web application for data integration in neuroscience research.. PubMed. 150. 317–21. 5 indexed citations
13.
Morse, Thomas M.. (2008). ModelDB in computational neuroscience education - a research tool as interactive educational media.. PubMed. 3(1). bmm1409–bmm1409. 2 indexed citations
14.
Crasto, Chiquito, Luis Marenco, Nan Liu, et al.. (2007). SenseLab: new developments in disseminating neuroscience information. Briefings in Bioinformatics. 8(3). 150–162. 22 indexed citations
15.
Lam, Hugo Y. K., Luis Marenco, Tim W. Clark, et al.. (2007). AlzPharm: integration of neurodegeneration data using RDF. BMC Bioinformatics. 8(S3). S4–S4. 36 indexed citations
16.
Morse, Thomas M.. (2007). Model sharing in computational neuroscience. Scholarpedia. 2(4). 3036–3036. 5 indexed citations
17.
Hines, Michael L., Thomas M. Morse, Michele Migliore, & Nicholas T. Carnevale. (2004). ModelDB: A Database to Support Computational Neuroscience. Journal of Computational Neuroscience. 17(1). 7–11. 265 indexed citations
18.
Davison, Andrew P., Thomas M. Morse, Michele Migliore, Gordon M. Shepherd, & Michael L. Hines. (2004). Semi-Automated Population of an Online Database of Neuronal Models (ModelDB) With Citation Information, Using PubMed for Validation. Neuroinformatics. 2(3). 327–332. 15 indexed citations
19.
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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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