Thomas A. Geddes

751 total citations
10 papers, 467 citations indexed

About

Thomas A. Geddes is a scholar working on Molecular Biology, Artificial Intelligence and Surgery. According to data from OpenAlex, Thomas A. Geddes has authored 10 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 2 papers in Artificial Intelligence and 1 paper in Surgery. Recurrent topics in Thomas A. Geddes's work include Single-cell and spatial transcriptomics (4 papers), Gene expression and cancer classification (3 papers) and Machine Learning in Bioinformatics (2 papers). Thomas A. Geddes is often cited by papers focused on Single-cell and spatial transcriptomics (4 papers), Gene expression and cancer classification (3 papers) and Machine Learning in Bioinformatics (2 papers). Thomas A. Geddes collaborates with scholars based in Australia, Denmark and United Kingdom. Thomas A. Geddes's co-authors include Pengyi Yang, Jean Yang, Yue Cao, Hani Jieun Kim, James G. Burchfield, Yingxin Lin, David E. James, Tai-Yun Kim, Benjamin L. Parker and Jørgen F. P. Wojtaszewski and has published in prestigious journals such as Bioinformatics, BMC Bioinformatics and eLife.

In The Last Decade

Thomas A. Geddes

9 papers receiving 456 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thomas A. Geddes Australia	7	242	83	45	43	33	10	467
Jakub Tomek United Kingdom	16	399 1.6×	95 1.1×	32 0.7×	66 1.5×	34 1.0×	37	1.1k
Yue Cao China	9	308 1.3×	94 1.1×	66 1.5×	77 1.8×	37 1.1×	39	564
Benjamin Ulfenborg Sweden	10	193 0.8×	124 1.5×	18 0.4×	62 1.4×	95 2.9×	26	560
Haoyang Li China	15	323 1.3×	134 1.6×	31 0.7×	73 1.7×	146 4.4×	38	720
Hui-Ling Huang Taiwan	16	402 1.7×	45 0.5×	46 1.0×	17 0.4×	12 0.4×	26	730
Shan Huang China	13	177 0.7×	25 0.3×	56 1.2×	19 0.4×	18 0.5×	37	463
Shibiao Wan United States	21	885 3.7×	81 1.0×	31 0.7×	53 1.2×	14 0.4×	54	1.1k
Parminder Singh Reel United Kingdom	8	304 1.3×	39 0.5×	11 0.2×	69 1.6×	67 2.0×	16	649
Tomasz Adamusiak United States	11	425 1.8×	140 1.7×	22 0.5×	33 0.8×	10 0.3×	17	594

Countries citing papers authored by Thomas A. Geddes

Since Specialization

Citations

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

Fields of papers citing papers by Thomas A. Geddes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas A. Geddes

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

All Works

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10 of 10 papers shown

Xiao, Di, Michael Lin, Chunlei Liu, et al.. (2023). SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data. NAR Genomics and Bioinformatics. 5(4). lqad099–lqad099. 1 indexed citations

Francis, Deanne, Shila Ghazanfar, Essi Havula, et al.. (2021). Genome-wide analysis in Drosophila reveals diet-by-gene interactions and uncovers diet-responsive genes. G3 Genes Genomes Genetics. 11(10). 6 indexed citations

Kearney, Alison L., Dougall M. Norris, Sean J. Humphrey, et al.. (2021). Akt phosphorylates insulin receptor substrate to limit PI3K-mediated PIP3 synthesis. eLife. 10. 53 indexed citations

Norris, Dougall M., Pengyi Yang, Sung‐Young Shin, et al.. (2021). Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression. iScience. 24(2). 102118–102118. 16 indexed citations

Kim, Hani Jieun, Yingxin Lin, Thomas A. Geddes, Jean Yang, & Pengyi Yang. (2020). CiteFuse enables multi-modal analysis of CITE-seq data. Bioinformatics. 36(14). 4137–4143. 62 indexed citations

Cao, Yue, Thomas A. Geddes, Jean Yang, & Pengyi Yang. (2020). Ensemble deep learning in bioinformatics. Nature Machine Intelligence. 2(9). 500–508. 220 indexed citations

Kim, Tai-Yun, Kitty Lo, Thomas A. Geddes, et al.. (2019). scReClassify: post hoc cell type classification of single-cell rNA-seq data. BMC Genomics. 20(S9). 913–913. 20 indexed citations

Geddes, Thomas A., Tai-Yun Kim, James G. Burchfield, et al.. (2019). Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis. BMC Bioinformatics. 20(S19). 660–660. 37 indexed citations

Kim, Tai-Yun, Kitty Lo, Thomas A. Geddes, et al.. (2019). scReClassify: post hoc cell type classification of single-cell RNA-seq data. Faculty of 1000 Research Ltd. 8.

10.

Parker, Benjamin L., James G. Burchfield, Daniel Clayton, et al.. (2017). Multiplexed Temporal Quantification of the Exercise-regulated Plasma Peptidome. Molecular & Cellular Proteomics. 16(12). 2055–2068. 52 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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