Stephen J. Goodswen

828 total citations
20 papers, 538 citations indexed

About

Stephen J. Goodswen is a scholar working on Molecular Biology, Parasitology and Ecology. According to data from OpenAlex, Stephen J. Goodswen has authored 20 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 10 papers in Parasitology and 8 papers in Ecology. Recurrent topics in Stephen J. Goodswen's work include vaccines and immunoinformatics approaches (9 papers), Bacteriophages and microbial interactions (8 papers) and Toxoplasma gondii Research Studies (8 papers). Stephen J. Goodswen is often cited by papers focused on vaccines and immunoinformatics approaches (9 papers), Bacteriophages and microbial interactions (8 papers) and Toxoplasma gondii Research Studies (8 papers). Stephen J. Goodswen collaborates with scholars based in Australia, India and Ireland. Stephen J. Goodswen's co-authors include John Ellis, Paul Kennedy, Joel Barratt, Alexa Kaufer, Cedric Gondro, Haja N. Kadarmideen, Nathan S. Watson‐Haigh, J. H. J. van der Werf and Stephen Bush and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Stephen J. Goodswen

20 papers receiving 526 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Stephen J. Goodswen Australia	12	266	195	103	76	67	20	538
Ahmad Nematollahi Iran	13	112 0.4×	222 1.1×	29 0.3×	88 1.2×	101 1.5×	49	488
Maurizio Viscardi Italy	14	161 0.6×	71 0.4×	83 0.8×	183 2.4×	93 1.4×	31	531
Yuehua Ke China	16	322 1.2×	44 0.2×	201 2.0×	115 1.5×	96 1.4×	64	809
M. Gill Hartley United Kingdom	13	363 1.4×	112 0.6×	188 1.8×	137 1.8×	80 1.2×	20	714
Sara M. Vetter United States	16	138 0.5×	186 1.0×	45 0.4×	142 1.9×	78 1.2×	25	570
Hüseyin Can Türkiye	15	150 0.6×	459 2.4×	272 2.6×	186 2.4×	49 0.7×	83	734
Xiaomin Zhao China	17	118 0.4×	125 0.6×	56 0.5×	172 2.3×	20 0.3×	49	701
Mert Döşkaya Türkiye	18	172 0.6×	585 3.0×	431 4.2×	227 3.0×	64 1.0×	91	957
Anne‐Sophie Le Guern France	11	197 0.7×	58 0.3×	54 0.5×	53 0.7×	34 0.5×	22	450
Hui Zhi United States	11	139 0.5×	239 1.2×	39 0.4×	159 2.1×	24 0.4×	16	505

Countries citing papers authored by Stephen J. Goodswen

Since Specialization

Citations

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

Fields of papers citing papers by Stephen J. Goodswen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen J. Goodswen. 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 Stephen J. Goodswen. The network helps show where Stephen J. Goodswen may publish in the future.

Co-authorship network of co-authors of Stephen J. Goodswen

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

All Works

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20 of 20 papers shown

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2023). A state-of-the-art methodology for high-throughput in silico vaccine discovery against protozoan parasites and exemplified with discovered candidates for Toxoplasma gondii. Scientific Reports. 13(1). 8243–8243. 6 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2023). A guide to current methodology and usage of reverse vaccinology towards in silico vaccine discovery. FEMS Microbiology Reviews. 47(2). 43 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2022). Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing. Scientific Reports. 12(1). 10349–10349. 4 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2021). Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning. Frontiers in Genetics. 12. 716132–716132. 4 indexed citations

Goodswen, Stephen J., et al.. (2021). Machine learning and applications in microbiology. FEMS Microbiology Reviews. 45(5). 134 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2021). Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis. Pathogens. 10(6). 660–660. 7 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2020). Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed. Methods in molecular biology. 2183. 29–42. 1 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2018). A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen. Frontiers in Genetics. 9. 332–332. 14 indexed citations

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2017). On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates. International Journal for Parasitology. 47(12). 779–790. 11 indexed citations

10.

Goodswen, Stephen J., Joel Barratt, Paul Kennedy, & John Ellis. (2015). Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine. International Journal for Parasitology. 45(5). 305–318. 7 indexed citations

11.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2014). Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics. 30(16). 2381–2383. 49 indexed citations

12.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2014). Discovering a vaccine against neosporosis using computers: is it feasible?. Trends in Parasitology. 30(8). 401–411. 17 indexed citations

13.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2014). Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores. PLoS ONE. 9(12). e115745–e115745. 16 indexed citations

14.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2013). A novel strategy for classifying the output from an in silicovaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms. BMC Bioinformatics. 14(1). 315–315. 37 indexed citations

15.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2012). Evaluating High-Throughput Ab Initio Gene Finders to Discover Proteins Encoded in Eukaryotic Pathogen Genomes Missed by Laboratory Techniques. PLoS ONE. 7(11). e50609–e50609. 26 indexed citations

16.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2012). A guide to in silico vaccine discovery for eukaryotic pathogens. Briefings in Bioinformatics. 14(6). 753–774. 28 indexed citations

17.

Goodswen, Stephen J., Paul Kennedy, & John Ellis. (2012). A review of the infection, genetics, and evolution of Neospora caninum: From the past to the present. Infection Genetics and Evolution. 13. 133–150. 109 indexed citations

18.

Ellis, John, Stephen J. Goodswen, Paul Kennedy, & Stephen Bush. (2012). The Core Mouse Response to Infection by Neospora Caninum Defined by Gene Set Enrichment Analyses. Bioinformatics and Biology Insights. 6. BBI.S9954–BBI.S9954. 1 indexed citations

19.

Goodswen, Stephen J., Cedric Gondro, Nathan S. Watson‐Haigh, & Haja N. Kadarmideen. (2010). FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases. BMC Bioinformatics. 11(1). 311–311. 22 indexed citations

20.

Goodswen, Stephen J., Cedric Gondro, Haja N. Kadarmideen, & J. H. J. van der Werf. (2010). Evaluating haplotype diversity within and between Australian sheep breeds. Research at the University of Copenhagen (University of Copenhagen). 947. 2 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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