Matthew A. Spence

800 total citations
25 papers, 456 citations indexed

About

Matthew A. Spence is a scholar working on Molecular Biology, Genetics and Materials Chemistry. According to data from OpenAlex, Matthew A. Spence has authored 25 papers receiving a total of 456 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Genetics and 5 papers in Materials Chemistry. Recurrent topics in Matthew A. Spence's work include Genomics and Phylogenetic Studies (4 papers), Evolution and Genetic Dynamics (4 papers) and RNA and protein synthesis mechanisms (4 papers). Matthew A. Spence is often cited by papers focused on Genomics and Phylogenetic Studies (4 papers), Evolution and Genetic Dynamics (4 papers) and RNA and protein synthesis mechanisms (4 papers). Matthew A. Spence collaborates with scholars based in Australia, United States and United Kingdom. Matthew A. Spence's co-authors include Colin J. Jackson, Joe A. Kaczmarski, Ashley M. Buckle, D.P. Boden, Nansook Hong, Matthew Wilding, Bùi Quang Minh, Joshua A. Mitchell, Alessandro T. Caputo and Li Lynn Tan and has published in prestigious journals such as Biochemistry, Journal of Power Sources and Chemical Communications.

In The Last Decade

Matthew A. Spence

24 papers receiving 445 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew A. Spence Australia 10 226 93 61 45 43 25 456
Xinyue Ma China 17 537 2.4× 142 1.5× 15 0.2× 53 1.2× 188 4.4× 53 790
Xinting Li China 18 432 1.9× 25 0.3× 8 0.1× 14 0.3× 221 5.1× 49 875
Maya Kleiman Israel 9 98 0.4× 78 0.8× 11 0.2× 12 0.3× 134 3.1× 25 660
Huihui Wang China 18 701 3.1× 135 1.5× 5 0.1× 13 0.3× 110 2.6× 43 951
Susan L. Nimmo United States 13 223 1.0× 9 0.1× 67 1.1× 6 0.1× 52 1.2× 16 513
Julie M. Lebert Canada 7 222 1.0× 84 0.9× 6 0.1× 4 0.1× 37 0.9× 7 424
Jinru Zhou China 14 407 1.8× 105 1.1× 6 0.1× 8 0.2× 124 2.9× 31 818
Xia Qiu China 12 248 1.1× 112 1.2× 18 0.3× 3 0.1× 55 1.3× 39 416
Mercedes Catalina Netherlands 12 501 2.2× 18 0.2× 5 0.1× 16 0.4× 30 0.7× 18 819
Franka Kálmán Switzerland 12 241 1.1× 15 0.2× 5 0.1× 24 0.5× 16 0.4× 20 473

Countries citing papers authored by Matthew A. Spence

Since Specialization
Citations

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

Fields of papers citing papers by Matthew A. Spence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew A. Spence. 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 Matthew A. Spence. The network helps show where Matthew A. Spence may publish in the future.

Co-authorship network of co-authors of Matthew A. Spence

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew A. Spence. A scholar is included among the top collaborators of Matthew A. Spence 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 Matthew A. Spence. Matthew A. Spence 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.
Spence, Matthew A., et al.. (2025). Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains. Biochemistry. 64(8). 1673–1684. 3 indexed citations
2.
Ji, Dawei, et al.. (2025). A Thermostable Bacterial Metallohydrolase that Degrades Organophosphate Plasticizers. ChemBioChem. 26(11). e202500055–e202500055.
3.
Mater, Adam C., et al.. (2025). Investigating the determinants of performance in machine learning for protein fitness prediction. Protein Science. 34(8). e70235–e70235. 1 indexed citations
4.
Spence, Matthew A., et al.. (2025). Protein evolution as a complex system. Nature Chemical Biology. 21(9). 1293–1299. 1 indexed citations
5.
Spence, Matthew A., et al.. (2024). Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors. Cell Systems. 15(4). 374–387.e6. 5 indexed citations
6.
Spence, Matthew A., Adam C. Mater, James Nichols, et al.. (2024). Leveraging ancestral sequence reconstruction for protein representation learning. Nature Machine Intelligence. 6(12). 1542–1555. 6 indexed citations
7.
Damry, Adam M., Matthew A. Spence, Rebecca L. Frkic, et al.. (2024). Increasing the Soluble Expression and Whole-Cell Activity of the Plastic-Degrading Enzyme MHETase through Consensus Design. Biochemistry. 63(13). 1663–1673. 6 indexed citations
8.
Spence, Matthew A., et al.. (2022). Comprehensive phylogenetic analysis of the ribonucleotide reductase family reveals an ancestral clade. eLife. 11. 12 indexed citations
9.
Spence, Matthew A., et al.. (2022). Analysis of insertions and extensions in the functional evolution of the ribonucleotide reductase family. Protein Science. 31(12). e4483–e4483. 7 indexed citations
10.
Spence, Matthew A., Li Lynn Tan, Joe A. Kaczmarski, et al.. (2022). Ancestral Sequence Reconstruction Identifies Structural Changes Underlying the Evolution of Ideonella sakaiensis PETase and Variants with Improved Stability and Activity. Biochemistry. 62(2). 437–450. 42 indexed citations
11.
Spence, Matthew A., et al.. (2021). A Comprehensive Phylogenetic Analysis of the Serpin Superfamily. Molecular Biology and Evolution. 38(7). 2915–2929. 44 indexed citations
12.
Spence, Matthew A., et al.. (2021). Ancestral sequence reconstruction for protein engineers. Current Opinion in Structural Biology. 69. 131–141. 99 indexed citations
13.
Kantor, Rami, John P Fulton, Jon A. Steingrimsson, et al.. (2020). Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health. PubMed. 12(s1). 3 indexed citations
14.
Novitsky, Vlad, Jon A. Steingrimsson, Mark Howison, et al.. (2020). Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters. Scientific Reports. 10(1). 18547–18547. 18 indexed citations
15.
Kaczmarski, Joe A., et al.. (2019). Structural and evolutionary approaches to the design and optimization of fluorescence-based small molecule biosensors. Current Opinion in Structural Biology. 57. 31–38. 24 indexed citations
16.
Wilding, Matthew, Nansook Hong, Matthew A. Spence, Ashley M. Buckle, & Colin J. Jackson. (2019). Protein engineering: the potential of remote mutations. Biochemical Society Transactions. 47(2). 701–711. 72 indexed citations
17.
Roberts, Andrew, et al.. (2019). THE OLDER POPULATION LIVING WITH AND PROVIDING CARE TO GRANDCHILDREN, BY NATIVITY: 2013-2017. Innovation in Aging. 3(Supplement_1). S267–S267. 1 indexed citations
18.
19.
Spence, Matthew A., et al.. (2001). Chemiluminescence and rotational alignment in Mn+O2: Direct observation of the MnO*(A′ 6Π) state. Physical Chemistry Chemical Physics. 3(17). 3610–3621. 4 indexed citations
20.
Spence, Matthew A. & Martin R. Levy. (1997). Rotational Alignment in MnO*(A6Σ+) from the Reaction Mn + O2. The Journal of Physical Chemistry A. 101(41). 7490–7498. 3 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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